# Overview
> [ccf-rankings](https://www.ccf.org.cn/en/About_CCF/Media_Center/) now marked with different colors(   )
>
> Newly added papers will be organized at the top of every category now.
### Related
* [Datasets Resources](https://paper.imzh.me/related/datasets)
* [Nice Expression](https://paper.imzh.me/related/nice-expressions)
* [Research Methods](https://paper.imzh.me/related/howtodoresearch) (*public information summery*)
* [Paper Resource Links](https://paper.imzh.me/related/papersource)
### Basics
* [图像处理基础知识](https://paper.imzh.me/basic-knowledge/image-processing)
### Backbone
**骨干网络**，多为图像分类的网络。
* [x] [Attention Is All You Need](https://paper.imzh.me/backbone/transformer) (*NeurIPS '17*) **\[**[**Paper**](https://proceedings.neurips.cc/paper/7181-attention-is-all)**]** **\[**[**Code\_official**](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py)**]** **\[**[**Code\_community**](https://github.com/jadore801120/attention-is-all-you-need-pytorch)**]**
* [x] [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://paper.imzh.me/backbone/efficientnet) (*ICML '19*) **\[**[**Paper**](https://arxiv.org/abs/1905.11946)**]** **\[**[**Code**](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet)**]**
* [x] [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://paper.imzh.me/backbone/vit) (*ICLR '21*) **\[**[**Paper**](https://arxiv.org/abs/2010.11929)**]** **\[**[**Code**](https://github.com/google-research/vision_transformer)**]**
* [x] [Multi-Dimensional Model Compression of Vision Transformer](https://paper.imzh.me/backbone/multi-dimensional-compression-vit) (*ICME '22*) **\[**[**Paper**](https://arxiv.org/abs/2201.00043)**]**
* [x] Deep Residual Learning for Image Recognition (*CVPR '16*) **\[**[**Paper**](https://arxiv.org/abs/1512.03385)**]**
* [ ] Generative Adversarial Networks (*NeurIPS '14*) **\[**[**Paper**](https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html)**]**
* [ ] Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond (*CVPR '23*) **\[**[**Paper**](https://feizc.github.io/resume/ganmae.pdf)**]**
* [ ] A Kernel Perspective of Skip Connections in Convolutional Networks (*ICLR '23*) **\[**[**Paper**](https://arxiv.org/abs/2211.14810)**]**
* [ ] EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2305.07027)**]** **\[**[**Code**](https://github.com/microsoft/Cream/tree/main/EfficientViT)**]** **\[**[**Note\_community**](https://blog.csdn.net/P_LarT/article/details/130687567)**]**
* [ ] Vision Transformers Need Registers *(ICLR '24)* **\[**[**Paper**](https://openreview.net/forum?id=2dnO3LLiJ1)**]**
* [ ] LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors *(ECCV '24)* **\[**[**Paper**](https://www.cs.umd.edu/~sakshams/LiFT/)**]**
* [ ] DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain [](https://arxiv.org/abs/2410.14980) [](https://github.com/w2kun/DCDepth)
* [ ] Segment Anything [](https://openaccess.thecvf.com/content/ICCV2023/html/Kirillov_Segment_Anything_ICCV_2023_paper.html) [](https://github.com/facebookresearch/segment-anything)
### Image Tampering
**图像篡改检测定位**
#### AIGC
2026
* [ ] Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance [](https://arxiv.org/abs/2602.06530)
* [ ] LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization [](https://arxiv.org/abs/2602.05577)
* [ ] MIRROR: Manifold Ideal Reference ReconstructOR for Generalizable AI-Generated Image Detection[](https://arxiv.org/abs/2602.02222) [](https://github.com/349793927/MIRROR)
* [ ] Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models [](https://arxiv.org/abs/2602.01738)
* [ ] Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection [](https://arxiv.org/abs/2602.10042)
#### Image Editing
2026
* [ ] SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks [](https://arxiv.org/abs/2601.20310)
* [ ] ForgeryVCR: Visual-Centric Reasoning via Efficient Forensic Tools in MLLMs for Image Forgery Detection and Localization [](https://arxiv.org/abs/2602.14098) [](https://youqiwong.github.io/projects/ForgeryVCR/)
* [ ] ForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detection [](https://arxiv.org/abs/2601.08873)
2025
* [ ] UGD-IML: A Unified Generative Diffusion-based Framework for Constrained and Unconstrained Image Manipulation Localization [](https://arxiv.org/abs/2508.06101)
* [ ] CLUE: Leveraging Low-Rank Adaptation to Capture Latent Uncovered Evidence for Image Forgery Localization [](https://arxiv.org/abs/2508.07413) [](https://github.com/SZAISEC/CLUE)
* [ ] ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack [](https://arxiv.org/abs/2508.07402) [](https://github.com/siriusPRX/ForensicsSAM)
* [ ] MUN: Image Forgery Localization Based on M³ Encoder and UN Decoder [](https://ojs.aaai.org/index.php/AAAI/article/view/32606) [](https://github.com/MrHuan3/MUN)
* [ ] ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization [](https://arxiv.org/abs/2505.11003) [](https://github.com/scu-zjz/ForensicHub)
* [ ] AFCMS-Net: Adaptive feature coupling and multi-level supervision network for effective image forgery localization [](https://doi.org/10.1016/j.knosys.2025.114126) [](https://github.com/SwallowIsXYZ/0617_AFCMS-Net)
* [ ] Edge-aware Affinity Enhancement for Image Manipulation Localization [](https://paper.imzh.me/readme)
* [ ] DFPD: Dual-Forgery Proactive Defense against Both Deepfakes and Traditional Image Manipulations [](https://paper.imzh.me/readme) [](https://github.com/imagecbj/DFPD)
* [ ] Beyond Fully Supervised Pixel Annotations: Scribble-Driven Weakly-Supervised Framework for Image Manipulation Localization [](https://arxiv.org/abs/2507.13018) [](https://github.com/vpsg-research/SCAF)
* [ ] Loupe: A Generalizable and Adaptive Framework for Image Forgery Detection [](https://arxiv.org/abs/2506.16819) [](https://github.com/Kamichanw/Loupe)
* [ ] M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization [](https://arxiv.org/abs/2506.20922)
* [ ] Active Adversarial Noise Suppression for Image Forgery Localization [](https://arxiv.org/abs/2506.12871) [](https://github.com/SZAISEC/ANSM)
* [ ] Can We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization through Spare-Coding Transformer [](https://arxiv.org/abs/2412.14598) [](https://github.com/scu-zjz/SparseViT)
* [ ] Let Images Speak More: An Efficient Method for Detecting Image Manipulation History [](https://ieeexplore.ieee.org/abstract/document/11007490) [](https://github.com/CherishL-J/Op-detection)
* [ ] EAN: Edge-Aware Network for Image Manipulation Localization [](https://ieeexplore.ieee.org/abstract/document/10705343)
* [ ] Unravelling Digital Forgeries: A Systematic Survey on Image Manipulation Detection and Localization [](https://doi.org/10.1145/3731243)
* [ ] Self-Optimization Training for Weakly Supervised Image Manipulation Localization [](https://ieeexplore.ieee.org/abstract/document/10889843)
* [ ] KLMN: Knowledge distillation based lightweight multi-clue image forgery detection and localization [](https://ieeexplore.ieee.org/abstract/document/10888764)
* [ ] Image manipulation localization via semantic-guided feature enhancement and deep multi-scale edge supervision [](https://doi.org/10.1016/j.neucom.2025.130255)
* [ ] Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization [](https://arxiv.org/abs/2504.05224)
* [ ] A Lightweight and Effective Image Tampering Localization Network with Vision Mamba [](https://arxiv.org/abs/2502.09941) [](https://github.com/multimediaFor/ForMa)
* [ ] Robustifying vision transformer for image forgery localization with multi-exit architectures [](https://www.sciencedirect.com/science/article/pii/S0031320325002250)
* [ ] A Semantically Impactful Image Manipulation Dataset: Characterizing Image Manipulations using Semantic Significance [](https://openaccess.thecvf.com/content/WACV2025/html/Chen_A_Semantically_Impactful_Image_Manipulation_Dataset_Characterizing_Image_Manipulations_using_WACV_2025_paper.html) [](https://github.com/csiimd/csiimd)
* [ ] FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models [](https://arxiv.org/abs/2410.02761) [](https://github.com/zhipeixu/FakeShield) *❗Updated*
* [ ] SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model [](http://arxiv.org/abs/2412.04292) [](https://github.com/hzlsaber/SIDA)*❗Updated*
* [ ] OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking [](https://arxiv.org/abs/2412.01615) [](https://github.com/xuanyuzhang21/OmniGuard)
* [ ] Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models [](https://arxiv.org/abs/2503.15024) [](https://github.com/Forensics-Bench/Forensics-Bench)
* [ ] Weakly-supervised cross-contrastive learning network for image manipulation detection and localization [](https://doi.org/10.1016/j.knosys.2025.113033)
* [ ] Dual-decoding branch contrastive augmentation for image manipulation localization [](https://www.sciencedirect.com/science/article/pii/S0950705124014102)
* [ ] PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization [](https://www.sciencedirect.com/science/article/pii/S0031320324008872)
* [ ] UFG-Net: Uncertainty and frequency guided network for image forgery localization [](https://doi.org/10.1016/j.neucom.2025.129471)
* [ ] PRest-Net: Multi-domain Probability Estimation Network for Robust Image Forgery Detection [](https://dl.acm.org/doi/pdf/10.1145/3711930)
* [ ] Exploring multi-scale forgery clues for stereo super-resolution image forgery localization [](https://doi.org/10.1016/j.patcog.2024.111230)
* [ ] Mesoscopic Insights: Orchestrating Multi-scale & Hybrid Architecture for Image Manipulation Localization [](https://arxiv.org/abs/2412.13753) [](https://github.com/scu-zjz/Mesorch)
* [ ] SAFIRE: Segment Any Forged Image Region [](https://arxiv.org/abs/2412.08197) [](https://github.com/mjkwon2021/SAFIRE)
2024
* [ ] Employing Reinforcement Learning to Construct a Decision-making Environment for Image Forgery Localization [](https://ieeexplore.ieee.org/abstract/document/10478835) [](https://github.com/tansq/CoDE)
* [ ] Learning Compressed Artifact for JPEG Manipulation Localization Using Wide-Receptive-Field Network [](https://doi.org/10.1145/3678883)
* [ ] Proactive image manipulation detection via deep semi-fragile watermark [](https://doi.org/10.1016/j.neucom.2024.127593)
* [ ] Multi-domain Probability Estimation Network for Forgery Detection over Online Social Network Shared Images [](https://ieeexplore.ieee.org/document/10687645)
* [ ] Dual Hypergraph Convolution Networks for Image Forgery Localization [](https://doi.org/10.1016/j.neucom.2024.128607)
* [ ] DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization [](https://doi.ieeecomputersociety.org/10.1109/TDSC.2024.3522190)
* [ ] SUMI-IFL: An Information-Theoretic Framework for Image Forgery Localization with Sufficiency and Minimality Constraints [](http://arxiv.org/abs/2412.09981)
* [ ] Image Forgery Localization with State Space Models [](https://arxiv.org/abs/2412.11214) [](https://github.com/multimediaFor/LoMa)
* [ ] PhotoHolmes: a Python library for forgery detection in digital images [](https://arxiv.org/abs/2412.14969) [](https://github.com/photoholmes/photoholmes)
* [ ] A Novel Universal Image Forensics Localization Model Based on Image Noise and Segment Anything Model [](https://doi.org/10.1145/3658664.3659639)
* [ ] Omni-IML: Towards Unified Image Manipulation Localization [](http://arxiv.org/abs/2411.14823)
* [ ] ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection [](http://arxiv.org/abs/2411.19466) [](https://github.com/sunzhihao18/ForgerySleuth)
* [ ] Dual JPEG Compatibility: a Reliable and Explainable Tool for Image Forensics [](http://arxiv.org/abs/2408.17106)
* [ ] Is JPEG AI going to change image forensics? [](https://arxiv.org/abs/2412.03261)
* [ ] Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation [](https://arxiv.org/abs/2412.01622)
* [ ] Image manipulation localization via dynamic cross-modality fusion and progressive integration [](https://doi.org/10.1016/j.neucom.2024.128607)
* [ ] HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning [](https://arxiv.org/abs/2410.21861)
* [ ] Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization (HiFi-Net++) [](https://arxiv.org/abs/2410.23556) [](https://github.com/CHELSEA234/HiFi_IFDL)
* [ ] Image Manipulation Detection With Implicit Neural Representation and Limited Supervision [](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/12164.pdf)
* [ ] AdaIFL: Adaptive Image Forgery Localization via a Dynamic and Importance-aware Transformer Network [](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06023.pdf)
* [ ] Noise-assisted Prompt Learning for Image Forgery Detection and Localization [](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/01688.pdf)
* [ ] Learning Universal Features for Generalizable Image Forgery Localization [](https://openreview.net/forum?id=OKzvovmUbh) [](https://github.com/ZhaoHengrun/GIFL)
* [ ] A Large-scale Interpretable Multi-modality Benchmark for Image Forgery Localization [](https://openreview.net/forum?id=7AvYFqcNfn)
* [ ] ForgeryTTT: Zero-Shot Image Manipulation Localization with Test-Time Training [](https://arxiv.org/abs/2410.04032)
* [ ] ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [](https://arxiv.org/abs/2410.10238)
* [ ] FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models [](https://arxiv.org/abs/2404.13306) [](https://github.com/Yixuan423/FakeBench)
* [ ] EL-FDL: Improving Image Forgery Detection and Localization via Ensemble Learning [](https://link.springer.com/chapter/10.1007/978-3-031-72335-3_17)
* [ ] Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [](https://arxiv.org/abs/2309.09667)
* [ ] Detecting and Grounding Multi-Modal Media Manipulation and Beyond [](https://ieeexplore.ieee.org/abstract/document/10440475/) [](https://github.com/rshaojimmy/MultiModal-DeepFake)
* [ ] FastForensics: Efficient Two-Stream Design for Real-Time Image Manipulation Detection [](https://doi.org/10.1016/j.knosys.2024.111988)
* [ ] Auto-focus tracing: Image manipulation detection with artifact graph contrastive [](https://doi.org/10.1016/j.knosys.2024.112545) [](https://github.com/pwy-cmd/AFGCL)
* [ ] LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification [](https://arxiv.org/abs/2407.18614)
* [ ] Image manipulation detection and localization using multi-scale contrastive learning [](https://doi.org/10.1016/j.asoc.2024.111914)
* [ ] Attentive and Contrastive Image Manipulation Localization With Boundary Guidance [](https://ieeexplore.ieee.org/document/10589438)
* [ ] Rethinking Image Editing Detection in the Era of Generative AI Revolution [](https://arxiv.org/abs/2311.17953) [](https://github.com/ICTMCG/GRE)
* [ ] Multi-view Feature Extraction via Tunable Prompts is Enough for Image Manipulation Localization [](https://openreview.net/forum?id=Ci5g2dnrMK)
* [ ] Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review [](https://arxiv.org/abs/2401.06999)
* [ ] TGIF: Text-Guided Inpainting Forgery Dataset [](http://arxiv.org/abs/2407.11566) [](https://github.com/IDLabMedia/tgif-dataset)
* [ ] Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization [](https://arxiv.org/abs/2406.13565) [](https://github.com/multimediaFor/MPC) *❗Updated*
* [ ] GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization [](https://arxiv.org/abs/2406.16531) [](https://github.com/chenyirui/GIM)
* [ ] IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization [](https://arxiv.org/abs/2406.10580) [](https://github.com/scu-zjz/IMDLBenCo)
* [ ] DH-GAN: Image manipulation localization via a dual homology-aware generative adversarial network [](https://doi.org/10.1016/j.patcog.2024.110658)
* [ ] DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention [](http://arxiv.org/abs/2406.01489)
* [ ] EC-Net: General image tampering localization network based on edge distribution guidance and contrastive learning [](https://doi.org/10.1016/j.knosys.2024.111656)
* [ ] Frequency-constrained transferable adversarial attack on image manipulation detection and localization [](https://link.springer.com/article/10.1007/s00371-024-03482-4)
* [ ] A Contribution-Aware Noise Feature representation model for image manipulation localization [](https://doi.org/10.1016/j.knosys.2024.111988)
* [ ] Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion [](https://arxiv.org/abs/2309.09306) [](https://github.com/multimediaFor/EITLNet)
* [ ] PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning [](https://arxiv.org/abs/2401.00653)
* [ ] Diffusion models meet image counter-forensics [](https://arxiv.org/abs/2311.13629) [](https://github.com/mtailanian/diff-cf)
* [ ] Research about the Ability of LLM in the Tamper-Detection Area [](https://arxiv.org/abs/2401.13504)
* [ ] Deep Image Restoration For Image Anti-Forensics [](https://arxiv.org/abs/2405.02751) [](https://github.com/99eren99/DIRFIAF)
* [ ] Deep Image Composition Meets Image Forgery [](https://arxiv.org/abs/2404.02897) [](https://github.com/99eren99/DIS25k)
* [ ] Fusion Transformer with Object Mask Guidance for Image Forgery Analysis (OMG-Fuser) [](https://arxiv.org/abs/2403.12229) [](https://github.com/mever-team/omgfuser)
* [ ] Exploring Multi-Modal Fusion for Image Manipulation Detection and Localization [](https://arxiv.org/abs/2312.01790) [](https://github.com/idt-iti/mmfusion-iml)
* [ ] A New Benchmark and Model for Challenging Image Manipulation Detection [](https://arxiv.org/abs/2311.14218) [](https://github.com/ZhenfeiZ/CIMD)
* [ ] MGQFormer: Mask-Guided Query-Based Transformer for Image Manipulation Localization [](https://ojs.aaai.org/index.php/AAAI/article/view/28520) [](https://dml.fudan.edu.cn/d1/65/c35285a643429/page.htm)
* [ ] Learning Discriminative Noise Guidance for Image Forgery Detection and Localization [](https://ojs.aaai.org/index.php/AAAI/article/view/28608)
* [ ] CatmullRom Splines-Based Regression for Image Forgery Localization [](https://ojs.aaai.org/index.php/AAAI/article/view/28548)
* [ ] UnionFormer: Unified-Learning Transformer with Multi-View Representation for Image Manipulation Detection and Localization [](https://openaccess.thecvf.com/content/CVPR2024/papers/Li_UnionFormer_Unified-Learning_Transformer_with_Multi-View_Representation_for_Image_Manipulation_Detection_CVPR_2024_paper.pdf)
* [ ] Towards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods [](https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_Towards_Modern_Image_Manipulation_Localization_A_Large-Scale_Dataset_and_Novel_CVPR_2024_paper.pdf) [](https://github.com/qcf-568/MIML)
* [ ] EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection [](https://arxiv.org/abs/2312.08883) [](https://github.com/xuanyuzhang21/EditGuard)
* [ ] DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization [](https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_DiffForensics_Leveraging_Diffusion_Prior_to_Image_Forgery_Detection_and_Localization_CVPR_2024_paper.pdf)
* [ ] IML-ViT: Image Manipulation Localization by Vision Transformer [](https://arxiv.org/abs/2307.14863) [](https://github.com/SunnyHaze/IML-ViT)
* [ ] CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data [](https://arxiv.org/abs/2401.13006)
2023
* [ ] Image Manipulation Detection Based on Ringed Residual Edge Artifact Enhancement and Multiple Attention Mechanisms [](https://link.springer.com/chapter/10.1007/978-981-99-8543-2_30)
* [ ] Improving CoatNet for Spatial and JPEG Domain Steganalysis (*ICME '23*) **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/10219598/)**]**
* [ ] A survey on deep learning-based image forgery detection *(PR '23)* **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0031320323004764)**]**
* [ ] PL-GNet: Pixel Level Global Network for detection and localization of image forgeries [](https://www.sciencedirect.com/science/article/pii/S092359652300111X) [](https://github.com/znshi/PL-GNet)
* [ ] Progressive Feedback-Enhanced Transformer for Image Forgery Localization *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2311.08910)**]** **\[**[**Code**](https://github.com/multimediaFor/ProFact)**]**
* [ ] Secondary Labeling A Novel Labeling Strategy for Image Manipulation Detection *(MM '23)* **\[**[**Paper**](https://doi.org/10.1145/3581783.3613839)**]**
* [ ] GP-Net: Image Manipulation Detection and Localization via Long-Range Modeling and Transformers (*Appl. Sci. (IF: 2.8, not included in CCFs), MDPI, '23*) **\[**[**Paper**](https://www.mdpi.com/2076-3417/13/21/12053)**]**
* [ ] DS-Net: Dual supervision neural network for image manipulation localization *(IET-IPR '23)* **\[**[**Paper**](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/ipr2.12885)**]**
* [ ] Learning to Immunize Images for Tamper Localization and Self Recovery *(TPAMI ‘23)* **\[**[**Paper**](https://arxiv.org/pdf/2210.15902.pdf)**]**
* [ ] Semantic-agnostic progressive subtractive network for image manipulation detection and localization *(Neurocomputing '23)* **\[**[**Paper**](https://doi.org/10.1016/j.neucom.2023.126263)**]**
* [ ] Towards Effective Image Manipulation Detection with Proposal Contrastive Learning *(TCSVT '23)* **\[**[**Paper**](https://arxiv.org/pdf/2210.08529.pdf)**]** **\[**[**Code**](https://github.com/Sandy-Zeng/PCL)**]**
* [ ] Effective image tampering localization with multi-scale ConvNeXt feature fusion (*JVCIR '23)* **\[**[**Paper**](https://arxiv.org/abs/2208.13739)**]** **\[**[**Code**](https://github.com/multimediaFor/ConvNeXtFF)**]**
* [ ] Evading Detection Actively: Toward Anti-Forensics against Forgery Localization (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2310.10036)**]** **\[**[**Code**](https://github.com/tansq/SEAR)**]**
* [ ] [ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization](https://paper.imzh.me/image-forgery/2023/reloc) (*TIFS '23*) **\[**[**Paper**](https://arxiv.org/abs/2211.03930)**]** **\[**[**Code**](https://github.com/ZhuangPeiyu/ReLoc)**]**
* [ ] Image manipulation detection by multiple tampering traces and edge artifact enhancement (*PR '23*) **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0031320322005064)**]** (*EMT-Net*)
* [x] [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://paper.imzh.me/image-forgery/2023/cfl-net) (*WACV '23*) **\[**[**Paper**](https://arxiv.org/abs/2210.02182)**]** **\[**[**Code**](https://github.com/niloy193/CFLNet)**]**
* [x] [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://paper.imzh.me/image-forgery/2023/trufor) (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2212.10957)**]** **\[**[**Project**](https://grip-unina.github.io/TruFor/)**]** **\[**[**Code**](https://github.com/grip-unina/TruFor)**]**
* [x] [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://paper.imzh.me/image-forgery/2023/tbformer) (*SPL '23*) **\[**[**Paper**](https://arxiv.org/abs/2302.13004)**]** **\[**[**Code**](https://github.com/free1dom1/tbformer)**]**
* [ ] [Detecting and Grounding Multi-Modal Media Manipulation](https://paper.imzh.me/image-forgery/2023/dgm4) (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.02556)**]** **\[**[**Code**](https://github.com/rshaojimmy/MultiModal-DeepFake)**]** **\[**[**Project**](https://rshaojimmy.github.io/Projects/MultiModal-DeepFake)**]**
* [x] [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://paper.imzh.me/image-forgery/2023/hifi_ifdl) (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.17111)**]** **\[**[**Code**](https://github.com/CHELSEA234/HiFi_IFDL)**]**
* [ ] [Edge-aware Regional Message Passing Controller for Image Forgery Localization](https://paper.imzh.me/image-forgery/2023/ermpc) (*CVPR '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Edge-Aware_Regional_Message_Passing_Controller_for_Image_Forgery_Localization_CVPR_2023_paper.pdf)**]** **\[**[**Video**](https://youtu.be/2pDR-hOFcQw)**]**
* [ ] AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics (*CVPRW '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.06870)**]** **\[**[**Dataset**](https://github.com/shanface33/autosplice_dataset)**]**
* [ ] TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization (*ICCV '23*) **\[**[**Paper**](https://arxiv.org/abs/2308.05264)**]** **\[**[**Code**](https://github.com/vimal-isi-edu/TrainFors)**]**
* [ ] Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning (*ICCV '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2023/html/Zhou_Pre-Training-Free_Image_Manipulation_Localization_through_Non-Mutually_Exclusive_Contrastive_Learning_ICCV_2023_paper.html)**]** **\[**[**Code**](https://github.com/Knightzjz/NCL-IML)**]**
* [ ] Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning (*ICCV '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2023/html/Zhai_Towards_Generic_Image_Manipulation_Detection_with_Weakly-Supervised_Self-Consistency_Learning_ICCV_2023_paper.html)**]** **\[**[**Code**](https://github.com/yhZhai/WSCL)**]** **\[**[**ResearchGate**](https://www.researchgate.net/publication/373686108_Towards_Generic_Image_Manipulation_Detection_with_Weakly-Supervised_Self-Consistency_Learning)**]**
* [ ] SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection (*ICCV '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2023/html/Sun_SAFL-Net_Semantic-Agnostic_Feature_Learning_Network_with_Auxiliary_Plugins_for_Image_ICCV_2023_paper.html)**]**
* [ ] Uncertainty-guided Learning for Improving Image Manipulation Detection (*ICCV '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2023/html/Ji_Uncertainty-guided_Learning_for_Improving_Image_Manipulation_Detection_ICCV_2023_paper.html)**]**
* [ ] Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2308.09307)**]** **\[**[**Code**](https://github.com/HighwayWu/FOCAL)**]**
* [ ] Pixel-Inconsistency Modeling for Image Manipulation Localization (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2310.00234)**]** [](https://ieeexplore.ieee.org/abstract/document/10883001/) [](https://github.com/ChenqiKONG/PIM)*❗Updated*
* [ ] Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2310.06525)**]**
* [ ] [On the Security of the One-and-a-Half-Class Classifier for SPAM Feature-Based Image Forensics](https://github.com/greatzh/Papers/blob/main/image-forgery/2023/spam_analysis.md) (*TIFS '23*) **(Traditional method, Analysis)** **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/10098583/)**]**
2022
* [ ] DS-UNet: A dual streams UNet for refined image forgery localization *(InfoS '22)* **\[**[**Paper**](https://dl.acm.org/doi/abs/10.1016/j.ins.2022.08.005)**]**
* [ ] MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization (*ArXiv '22*) **\[**[**Paper**](https://arxiv.org/abs/2211.03140)**]**
* [ ] Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation (*TPAMI '22*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9904872)**]**
* [ ] ESRNet: Efficient Search and Recognition Network for Image Manipulation Detection (*TOMCCAP '22*) **\[**[**Paper**](https://doi.org/10.1145/3506853)**]** **\[**[**Tool**](https://github.com/tampered816/rrr)**]**
* [ ] Learning to localize image forgery using end-to-end attention network (*Neurocomputing '22*) **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0925231222011274)**]** **\[**[**Code**](https://github.com/sadaf-ali/-Learning-to-Localize-Image-Forgery-Using-End-to-End-Attention-Network)**]**
* [ ] MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection (*TPAMI '22*) **\[**[**Paper**](https://arxiv.org/abs/2112.08935)**]** **\[**[**Code**](https://github.com/dong03/MVSS-Net)**]**
* [ ] [Robust Image Forgery Detection Over Online Social Network Shared Images](https://paper.imzh.me/image-forgery/2022/ifosn) (*CVPR '22*) **\[**[**Paper**](https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Robust_Image_Forgery_Detection_Over_Online_Social_Network_Shared_Images_CVPR_2022_paper.pdf)**]** **\[**[**Code**](https://github.com/HighwayWu/ImageForensicsOSN)**]**
* [x] [ObjectFormer for Image Manipulation Detection and Localization](https://paper.imzh.me/image-forgery/2022/objectformer) (*CVPR '22*) **\[**[**Paper**](https://arxiv.org/abs/2203.14681)**]**
* [ ] GCA-Net: Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection (*CVPRW '22*) **\[**[**Paper**](https://arxiv.org/abs/2112.04298)**]**
* [ ] Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database (*WACV '22*) **\[**[**Paper**](https://arxiv.org/abs/2105.02700)**]** **\[**[**Code**](https://github.com/qbammey/trace)**]**
* [ ] [JPEG Compression-aware Image Forgery Localization](https://paper.imzh.me/image-forgery/2022/caifl) (*MM '22*) **\[**[**Paper**](https://dl.acm.org/doi/abs/10.1145/3503161.3547749)**]**
* [ ] [Image Manipulation Localization Using Multi-Scale Feature Fusion and Adaptive Edge Supervision](https://paper.imzh.me/image-forgery/2022/msff) (*TMM '22*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9996125/)**]**
* [x] [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://paper.imzh.me/image-forgery/2022/pscc-net) (*TCSVT '22*) **\[**[**Paper**](https://arxiv.org/abs/2103.10596)**]** **\[**[**Code**](https://github.com/proteus1991/PSCC-Net)**]**
* [x] [Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localization](https://paper.imzh.me/image-forgery/2022/satfl) (*TIFS '22*) **\[**[**Paper**](https://arxiv.org/abs/2107.02434)**]** **\[**[**Code**](https://github.com/tansq/SATFL)**]** (*LocateNet / SATFL*)
* [ ] M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection (*ICMR '22*) **\[**[**Paper**](https://arxiv.org/abs/2104.09770)**]** **\[**[**Code**](https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection)**]**
* [ ] A Principled Design of Image Representation: Towards Forensic Tasks (*TPAMI '22*) **\[**[**Paper**](https://arxiv.org/abs/2203.00913)**]** **\[**[**Code**](https://github.com/ShurenQi/DIR)**]**
* [ ] [TBNet: A Two-stream Boundary-aware Network for Generic Image Manipulation Localization](https://paper.imzh.me/image-forgery/2022/tbanet) (*KDE '22*) **\[**[**Paper**](https://arxiv.org/abs/2108.04508)**]**
* [ ] [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://paper.imzh.me/image-forgery/2022/catnetv2) (*IJCV '22*) **\[**[**Paper**](https://arxiv.org/abs/2108.12947)**]** **\[**[**Code**](https://github.com/mjkwon2021/CAT-Net)**]**
* [ ] Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection (*arXiv '22*) (**Survey**) **\[**[**Paper**](https://arxiv.org/abs/2212.05667)**]**
* [ ] Generic Image Manipulation Localization through the Lens of Multi-scale Spatial Inconsistence *(MM '22)* **\[**[**Paper**](http://dl.acm.org/citation.cfm?id=3548100)**]**
2021
* [ ] Multi-modality image manipulation detection (*ICME '21*) **\[**[**Paper**](https://doi.org/10.1109/ICME51207.2021.9428232)**]**
* [ ] MSTA-Net: Forgery Detection by Generating Manipulation Trace Based on Multi-Scale Self-Texture Attention (*TCSVT '21*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9643421)**]**
* [ ] Image Manipulation Detection by Multi-View Multi-Scale Supervision (*ICCV '21*) **\[**[**Paper**](https://arxiv.org/abs/2104.06832)**]** **\[**[**Code**](https://github.com/dong03/MVSS-Net)**]**
* [x] [TransForensics: Image Forgery Localization with Dense Self-Attention](https://paper.imzh.me/image-forgery/2021/transforensics) (*ICCV '21*) **\[**[**Paper**](https://arxiv.org/abs/2108.03871)**]**
* [ ] Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images (*ICCV '21*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2021/html/Rao_Self-Supervised_Domain_Adaptation_for_Forgery_Localization_of_JPEG_Compressed_Images_ICCV_2021_paper.html)**]**
* [ ] Image Tampering Localization Using a Dense Fully Convolutional Network (*TIFS '21*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9393396)**]** **\[**[**Code**](https://github.com/ZhuangPeiyu/Dense-FCN-for-tampering-localization)**]** (*DenseFCN*)
* [ ] Image Manipulation Localization Using Attentional Cross-Domain CNN Features (*TNNLS '21*) **\[**[**Paper**](https://doi.org/10.1109/TNNLS.2021.3130168)**]**
2020 and before
* [ ] A Full-Image Full-Resolution End-to-EndTrainable CNN Framework for Image Forgery Detection (*IEEE Access '20*) \*\*\[[Paper](https://paper.imzh.me/readme)]
* [ ] Constrained R-CNN: A general image manipulation detection model **\[**[**Paper**](https://arxiv.org/abs/1911.08217)**]** **\[**[**Code**](https://github.com/VedantWani/Constrained-R-CNN)**]**
* [ ] A CNNBased Camera Model Fingerprint (*TIFS '20*) \*\*\[[Paper](https://paper.imzh.me/readme)]
* [ ] An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics (*CVPR '20*) **\[**[**Paper**](http://openaccess.thecvf.com/content_CVPR_2020/html/Bammey_An_Adaptive_Neural_Network_for_Unsupervised_Mosaic_Consistency_Analysis_in_CVPR_2020_paper.html)**]** **\[**[**Code**](https://github.com/qbammey/adaptive_cfa_forensics)**]**
* [ ] A dense u-net with cross-layer intersection for detection and localization of image forgery (*ICASSP '20*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9054068)**]** **\[**[**Note\_unofficial**](https://blog.csdn.net/weixin_45366180/article/details/128413821)**]**
* [x] [Generate, Segment, and Refine: Towards Generic Manipulation Segmentation](https://paper.imzh.me/image-forgery/2020/gsrnet) (*AAAI '20*) **\[**[**Paper**](https://arxiv.org/abs/1811.09729)**]** **\[**[**Code**](https://github.com/pengzhou1108/GSRNet)**]** (*GSRNet*)
* [ ] SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization (*ECCV '20*) **\[**[**Paper**](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660307.pdf)**]** **\[**[**Code**](https://github.com/tsaishien-chen/SPAN)**]** [](http://media.ee.ntu.edu.tw/research/SPAN/)
* [ ] [Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries](https://paper.imzh.me/image-forgery/2019/hled) (*TIP '19*) **\[**[**Paper**](https://arxiv.org/abs/1903.02495)**]** **\[**[**Code**](https://github.com/jawadbappy/forgery_localization_HLED)**]**
* [ ] ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (*CVPR '19*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/8953774)**]** **\[**[**Code**](https://github.com/ISICV/ManTraNet)**]**
* [ ] Learning Rich Features for Image Manipulation Detection (*CVPR '18*) **\[**[**Paper**](https://arxiv.org/abs/1805.04953)**]** **\[**[**Code**](https://github.com/LarryJiang134/Image_manipulation_detection)**]**
#### CNN-synthesized
*Some of the above papers also contain methods to detect tampered images generated by GANs or DMs or **LLMs related** for synthetic images*
* [ ] *Frequency*-*aware Correlation Discovering* and Spatial Forgery Clue Distilling for Synthetic Image Detection [](https://paper.imzh.me/readme)
* [ ] ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection [](https://arxiv.org/abs/2408.13697) [](https://github.com/Yingjian-Chen/ForgeLens)
* [ ] ImgEdit: A Unified Image Editing Dataset and Benchmark [](https://arxiv.org/abs/2505.20275) [](https://github.com/PKU-YuanGroup/ImgEdit)
* [ ] Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection [](https://arxiv.org/abs/2411.15633) [](https://github.com/YZY-stack/Effort-AIGI-Detection)
* [ ] MFF-Net: A multi-view feature fusion network for generalized forgery image detection [](https://www.sciencedirect.com/science/article/pii/S0925231225010239) [](https://github.com/DragonM096/MFF-Net/)
* [ ] STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification [](https://icml.cc/virtual/2025/poster/45231)
* [ ] So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection [](https://arxiv.org/abs/2505.18660) [](https://github.com/hzlsaber/So-Fake/)
* [ ] Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective [](https://arxiv.org/abs/2505.22604)
* [ ] Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection [](https://ieeexplore.ieee.org/abstract/document/10890455)
* [ ] Noise-Informed Diffusion-Generated Image Detection With Anomaly Attention [](https://ieeexplore.ieee.org/abstract/document/11018089)
* [ ] FAMSeC: A Few-Shot-Sample-Based General AI-Generated Image Detection Method [](https://ieeexplore.ieee.org/abstract/document/10777302)
* [ ] FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics [](https://arxiv.org/abs/2503.24267) [](https://github.com/Yixuan423/FakeScope)
* [ ] Exploring the Collaborative Advantage of Low-level Information on Generalizable AI-Generated Image Detection [](https://arxiv.org/abs/2504.00463)
* [ ] AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era [](https://arxiv.org/abs/2504.11015) [](https://github.com/FlyTweety/AnimeDL2M)
* [ ] Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics [](https://arxiv.org/abs/2504.11686)
* [ ] Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach [](https://arxiv.org/abs/2504.11922) [](https://github.com/clpbc/BR-Gen)
* [ ] FakeReasoning: Towards Generalizable Forgery Detection and Reasoning [](https://arxiv.org/abs/2503.21210)
* [ ] Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation [](http://arxiv.org/abs/2503.14905) [](https://github.com/opendatalab/FakeVLM)
* [ ] LEGION: Learning to Ground and Explain for Synthetic Image Detection [](https://arxiv.org/abs/2503.15264) [](https://github.com/opendatalab/LEGION)
* [ ] Survey on AI-Generated Media Detection: From Non-MLLM to MLLM [](https://arxiv.org/abs/2502.05240)
* [ ] A Sanity Check for AI-generated Image Detection [](https://arxiv.org/abs/2406.19435) [](https://github.com/shilinyan99/AIDE)
* [ ] MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs [](https://openreview.net/forum?id=D6zn6ozJs7)
* [ ] Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection *(CVPR '24)* **\[**[**Paper**](https://arxiv.org/abs/2312.16649)**]**
* [ ] Preserving Fairness Generalization in Deepfake Detection *(CVPR '24)* **\[**[**Paper**](https://arxiv.org/abs/2402.17229)**]** **\[**[**Code**](https://github.com/Purdue-M2/Fairness-Generalization)**]**
* [ ] Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection *(ECCV '24)* **\[**[**Paper**](https://arxiv.org/abs/2402.19091)**]** **\[**[**Code**](https://github.com/mever-team/rine)**]**
* [ ] Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2312.16649)**]**
* [ ] AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2310.17419)**]** **\[**[**Code**](https://github.com/nctu-eva-lab/AntifakePrompt)**]**
* [ ] MaLP: Manipulation Localization Using a Proactive Scheme (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.16976)**]** **\[**[**Code**](https://github.com/vishal3477/pro_loc)**]**
* [ ] Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (*IJCAI '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.13349)**]** **\[**[**Code**](https://github.com/znshi/DisGRL)**]**
* [ ] Generalizable Synthetic Image Detection via Language-guided Contrastive Learning (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2305.13800)**]** **\[**[**Code**](https://github.com/HighwayWu/LASTED)**]**
* [ ] Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2306.17075)**]** **\[**[**Code**](https://github.com/laiyingxin2/DADF)**]**
* [ ] Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.13349)**]**
* [ ] Masked Relation Learning for DeepFake Detection (*TIFS '23*) **\[**[**Paper**](https://doi.org/10.1109/TIFS.2023.3249566)**]**
### Image Splicing
**图像的拼接篡改检测定位**
* [ ] Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approach [](https://arxiv.org/abs/2602.10079)
* [ ] Image splicing localization method driven by device difference feature guidance [](https://doi.org/10.1016/j.patcog.2025.112002)
2024
* [ ] Image splicing forgery detection: A review [](https://link.springer.com/article/10.1007/s11042-024-18801-z)
* [ ] Multi-Scale Cross-Fusion and Edge-Supervision Network for Image Splicing Localization [](https://arxiv.org/abs/2412.12503)
* [ ] D-Net: A dual-encoder network for image splicing forgery detection and localization [](https://arxiv.org/abs/2012.01821)
* [ ] UGEE-Net: Uncertainty-Guided and Edge-Enhanced Network for Image Splicing Localization (*Neural Networks '24*) **\[**[**Paper**](https://doi.org/10.1016/j.neunet.2024.106430)**]** **\[**[**Dataset**](https://github.com/QixianHao/-HTSI12K-dataset)**]**
* [ ] Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics (*arXiv '24*) **\[**[**Paper**](https://arxiv.org/abs/2404.16296)**]**
* [ ] A Visually Attentive Splice Localization Network with Multi-Domain Feature Extractor and Multi-Receptive Field Upsampler (*arXiv '24*) **\[**[**Paper**](https://arxiv.org/abs/2401.06995)**]**
* [ ] Feature Aggregation and Region-Aware Learning for Detection of Splicing Forgery *(SPL '24)* **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/10378732/)**]**
* [ ] Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset *( Signal, Image and Video Processing (IF: 2.3, not included in CCFs), '24 )* **\[**[**Paper**](https://arxiv.org/abs/2401.06998)**]**
2023
* [ ] GreatSplicing: A Semantically Rich Splicing Dataset *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2310.10070)**]** **\[**[**Dataset**](http://www.greatsplicing.net/)**]**
* [ ] Multi-scale attention context-aware network for detection and localization of image splicing: Efficient and robust identification network *(Appl. Intell. 23')* **\[**[**Paper**](https://link.springer.com/article/10.1007/s10489-022-04421-3)**]**
* [ ] A Multi-Stream Fusion Network for Image Splicing Localization *(MMM '23)* **\[**[**Paper**](https://arxiv.org/abs/2212.01128)**]**
* [ ] Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces *(TIFS '23)* **\[**[**Paper**](https://arxiv.org/abs/2211.12314)**]** **\[**[**IEEE Paper**](https://doi.org/10.1109/TIFS.2023.3346312)**]**
* [ ] Biomedical Image Splicing Detection using Uncertainty-Guided Refinement *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2309.16388)**]**
* [ ] A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network (*Applied Science (IF: 2.8, not included in CCFs), MDPI, '23*) **\[**[**Paper**](https://www.mdpi.com/2076-3417/13/3/1272)**]**
* [ ] Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization (*MM '23*) **\[**[**Paper**](https://arxiv.org/abs/2308.09357)**]**
2022
* [x] [Multi-Task SE-Network for Image Splicing Localization](https://paper.imzh.me/image-splicing/multi-task-se-network) (*TCSVT '22*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9591639)**]** **\[**[**Code**](https://github.com/YulansZhang/Multi-task-SE-Network-for-Image-Splicing-Localization)**]**
* [x] [ET: Edge-Enhanced Transformer for Image Splicing Detection](https://paper.imzh.me/image-splicing/et) (*SPL '22*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9769936)**]**
* [x] [Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U-net based on multiple spaces](https://paper.imzh.me/image-splicing/san-and-hdu-net) (*IJIS '22*) **\[**[**Paper**](https://doi.org/10.1002/int.22939)**]** **\[**[**Code**](https://github.com/yelusaleng/SAN_and_HDU-Net)**]**
* [x] [SISL:Self-Supervised Image Signature Learning for Splicing Detection & Localization](https://paper.imzh.me/image-splicing/sisl) (*CVPRW '22*) **\[**[**Paper**](https://arxiv.org/abs/2203.07824)**]**
* [ ] Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images (*TIFS '22*) **\[**[**Paper**](https://arxiv.org/abs/2206.10737)**]**
* [ ] Coarse-to-fine-grained method for image splicing region detection (*PR '22*) **\[**[**Paper**](https://doi.org/10.1016/j.patcog.2021.108347)**]**
2021
* [x] [CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing](https://paper.imzh.me/image-splicing/cat-net) (*WACV '21*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9423390)**]** **\[**[**Code**](https://github.com/mjkwon2021/CAT-Net)**]**
* [ ] Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering (*TIFS '21*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/9622213)**]** **\[**[**Code**](https://github.com/andreacos/CnnJpegPrimaryQuantizationEstimation)**]**
* [ ] Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing (*Data, MDPI '21*) **\[**[**Paper**](https://arxiv.org/abs/2108.09674)**]**
* [ ] Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization (*ICCV '21*) **\[**[**Paper**](http://openaccess.thecvf.com/content/ICCV2021/html/Bi_Reality_Transform_Adversarial_Generators_for_Image_Splicing_Forgery_Detection_and_ICCV_2021_paper.html)**]**
* [x] [Multi-Task Wavelet Corrected Network for Image Splicing Forgery Detection and Localization](https://paper.imzh.me/image-splicing/mwc-net) (*ICME '21*) **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/9428466/)**]**
* [ ] Detection and Localization of Multiple Image Splicing Using MobileNet V1 (*IEEE Access '21*) **\[**[**Paper**](https://arxiv.org/abs/2108.09674)**]**
2020 and before
* [ ] D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization *(arXiv '20)* **\[**[**Paper**](https://arxiv.org/abs/2012.01821)**]**
* [ ] Exposing splicing forgery in realistic scenes using deep fusion network (*InfoS '20*) **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0020025520302796)**]**
* [ ] Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor (*Neurocomputing '20*) **\[**[**Paper**](https://www.sciencedirect.com/science/article/abs/pii/S0925231220300278)**]**
* [ ] Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution (*TIFS '19*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/8658131)**]** **\[**[**Code**](https://github.com/yaqiliu-cs/CISDL-DMAC)**]**
* [ ] [RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection](https://paper.imzh.me/image-splicing/rru-net) (*CVPRW' 19*) **\[**[**Paper**](http://openaccess.thecvf.com/content_CVPRW_2019/html/CV-COPS/Bi_RRU-Net_The_Ringed_Residual_U-Net_for_Image_Splicing_Forgery_Detection_CVPRW_2019_paper.html?ref=https://githubhelp.com)**]** **\[**[**Code**](https://github.com/yelusaleng/RRU-Net)**]**
* [ ] Mixed adversarial generators for image splice detection (*NeuIPS '19*) **\[**[**Paper**](https://papers.nips.cc/paper/8315-the-point-where-reality-meets-fantasy-mixed-adversarial-generators-for-image-splice-detection)**]** **\[**[**Code**](https://github.com/vlkniaz/MAGritte)**]**
* [ ] Image Splicing Localization via Semi-global Network and Fully Connected Conditional Random Fields (*ECCV '18*)
* [ ] Image splicing localization using a multi-task fully convolutional network (mfcn) (*JVCIR '18*) **\[**[**Paper**](https://arxiv.org/abs/1709.02016)**]** **\[**[**Code**](https://github.com/namtpham/image_tampering_detection_references.git)**]**
* [x] [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://paper.imzh.me/image-splicing/self-consistency) (*ECCV '18*) **\[**[**Paper**](https://openaccess.thecvf.com/content_ECCV_2018/html/Jacob_Huh_Fighting_Fake_News_ECCV_2018_paper.html)**]** **\[**[**Code**](https://github.com/minyoungg/selfconsistency)**]**
* [ ] Deep Fusion Network for Splicing Forgery Localization (*ECCV '18*)
* [ ] Deep matching and validation network: An end-to-end solution to constrained image splicing localization and detection (*MM '17*) **\[**[**Paper**](https://arxiv.org/abs/1705.09765)**]**
### Image Harmonization
**图像协调化**
* [ ] Toward Realistic Image Compositing with Adversarial Learning (*CVPR '19*) **\[**[**Paper**](http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Toward_Realistic_Image_Compositing_With_Adversarial_Learning_CVPR_2019_paper.html)**]** **\[**[**Code\_unofficial**](https://github.com/SuhyeonHa/GCC-GANs)**]**
* [x] [Image Harmonization with Transformer](https://paper.imzh.me/image-harmonization/ht-d-ht) (*ICCV '21*) **\[**[**Paper**](http://openaccess.thecvf.com/content/ICCV2021/html/Guo_Image_Harmonization_With_Transformer_ICCV_2021_paper.html)**]**
* [ ] SSH: A Self-Supervised Framework for Image Harmonization (*ICCV '21*) **\[**[**Paper**](https://arxiv.org/abs/2108.06805)**]** **\[**[**Code**](https://github.com/VITA-Group/SSHarmonization)**]**
* [ ] Image Harmonization with Region-wise Contrastive Learning (*ArXiv '22*) **\[**[**Paper**](https://arxiv.org/abs/2205.14058)**]**
* [ ] [Harmonizer: Learning to Perform White-Box Image and Video Harmonization](https://paper.imzh.me/image-harmonization/harmonizer) (*ECCV '22*) **\[**[**Paper**](https://arxiv.org/abs/2207.01322)**]** **\[**[**Code**](https://github.com/ZHKKKe/Harmonizer)**]**
* [ ] Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization (*ECCV '22*) **\[**[**Paper**](https://arxiv.org/abs/2109.05750)**]** **\[**[**Code**](https://github.com/stefanLeong/S2CRNet)**]**
* [ ] High-Resolution Image Harmonization via Collaborative Dual Transformations (*CVPR '22*) **\[**[**Paper**](https://arxiv.org/abs/2109.06671)**]** **\[**[**Code\_unofficial**](https://github.com/SuhyeonHa/CDTNet-PyTorch)**]**
* [ ] [SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization](https://paper.imzh.me/image-harmonization/scs-co) (*CVPR '22*) **\[**[**Paper**](https://arxiv.org/abs/2204.13962)**]** **\[**[**Code**](https://github.com/YCHang686/SCS-Co-CVPR2022)**]**
* [ ] PCT-Net: Full Resolution Image Harmonization Using Pixel-Wise Color Transformations (*CVPR '23*)
* [ ] Semi-supervised Parametric Real-world Image Harmonization (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.00157)**]** **\[**[**Code**](https://people.eecs.berkeley.edu/~kewang/)**]** **\[**[**Project**](https://people.eecs.berkeley.edu/~kewang/sprih/)**]**
* [ ] LEMaRT: Label-Efficient Masked Region Transform for Image Harmonization(*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.13166)**]**
* [ ] Burstormer: Burst Image Restoration and Enhancement Transformer (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2304.01194)**] \[**[**Code**](https://github.com/akshaydudhane16/Burstormer.git)**]**
### Video Forgery
* [ ] Explainable Manipulated Videos Detection Using Multimodal Large Language Models [](https://dl.acm.org/doi/10.1145/3701716.3715283)
* [ ] Real-Time Video Forgery Detection via Vision-WiFi Silhouette Correspondence [](https://ieeexplore.ieee.org/abstract/document/10726788)
* [ ] Dual-PST: Dual-Branch SpatioTemporal-Planar Network for Video Forgery Detection [](https://ieeexplore.ieee.org/abstract/document/10890154)
* [ ] AvatarShield: Visual Reinforcement Learning for Human-Centric Video Forgery Detection [](https://arxiv.org/abs/2505.15173) [](https://github.com/zhipeixu/AvatarShield)
* [ ] SEDN: A Spatiotemporal Encoder-Decoder Network for End-to-End Object Removal Forgery Detection in High-Resolution Videos [](https://ieeexplore.ieee.org/document/10814702/)
* [ ] BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation [](https://arxiv.org/abs/2505.12620) [](https://github.com/l8cv/BusterX)
* [ ] Face Forgery Video Detection via Temporal Forgery Cue Unraveling [](https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Face_Forgery_Video_Detection_via_Temporal_Forgery_Cue_Unraveling_CVPR_2025_paper.html) [](https://github.com/zhenglab/TFCU)
* [ ] Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning [](https://arxiv.org/abs/2408.17065) [](https://github.com/YZY-stack/StA4Deepfake)
### Face Forgery
**人脸篡改**，篡改方法以及检测问题
* [ ] RCDN: Real-Centered Detection Network for Robust Face Forgery Identification [](http://arxiv.org/abs/2601.12111) [](https://github.com/wyattmccurdy12/Face-Forgery-Detection)
* [ ] CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization [](http://arxiv.org/abs/2602.02175)
* [ ] Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection [](http://arxiv.org/abs/2602.01854)
* [ ] OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [](http://arxiv.org/abs/2602.10687) [](https://github.com/shen8424/OmniVL-Guard)
2025
* [ ] Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization [](https://arxiv.org/abs/2408.02306)
* [ ] HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection [](https://arxiv.org/abs/2507.20913)
* [ ] Semantic Token Transformer for Face Forgery Detection [](https://ieeexplore.ieee.org/abstract/document/10988609)
* [ ] DeCLIP: Decoding CLIP Representations for Deepfake Localization [](https://arxiv.org/abs/2409.08849)
* [ ] Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection [](https://arxiv.org/pdf/2411.11396) [](https://github.com/beautyremain/SUR-LID)
* [ ] Towards General Visual-Linguistic Face Forgery Detection [](https://arxiv.org/abs/2307.16545) [](https://github.com/skjack/vlffd)
* [ ] Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection [](https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Forensics_Adapter_Adapting_CLIP_for_Generalizable_Face_Forgery_Detection_CVPR_2025_paper.html) [](https://github.com/OUC-VAS/ForensicsAdapter)
2024 and before
* [ ] FD-GAN: Generalizable and Robust Forgery Detection via Generative Adversarial Networks [](https://link.springer.com/article/10.1007/s11263-024-02136-1)
* [ ] DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion [](https://arxiv.org/pdf/2410.04372) [](https://github.com/skJack/DiffusionFake)
* [ ] Can We Leave Deepfake Data Behind in Training Deepfake Detector? [](http://arxiv.org/abs/2408.17052)
* [ ] Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [](http://arxiv.org/abs/2408.12791)
* [ ] Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues [](https://arxiv.org/abs/2212.14629) [](https://github.com/EdWhites/HFC-MFFD)
* [ ] Identity-Driven Multimedia Forgery Detection via Reference Assistance [](https://openreview.net/forum?id=aspe8HE0ZA)
* [ ] MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection (*TMM '23*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/10141892)**]**
* [ ] Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection *(AAAI '24)* **\[**[**Paper**](https://arxiv.org/abs/2403.01786)**]** **\[**[**Code**](https://github.com/QingyuLiu/Exposing-the-Deception)**]**
* [ ] Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking *(CVPR '24)*
* [ ] Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection *(CVPR '24)* **\[**[**Paper**](https://arxiv.org/abs/2311.11278)**]**
* [ ] Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection *(ICLR '24)* **\[**[**Paper**](https://openreview.net/pdf?id=8iTpB4RNvP)**]** **\[**[**Code**](https://github.com/JWLiang007/PFF)**]**
* [ ] Improving Fairness in Deepfake Detection *(WACV '24)* **\[**[**Paper**](https://arxiv.org/abs/2306.16635)**]** **\[**[**Code**](https://github.com/littlejuyan/DF_Fairness)**]**
* [ ] Weakly-Supervised Deepfake Localization in Diffusion-Generated Images *(WACV '24)* **\[**[**Paper**](https://arxiv.org/abs/2311.04584)**]** **\[**[**Code**](https://github.com/bit-ml/dolos)**]**
* [ ] Controllable Guide-Space for Generalizable Face Forgery Detection (*ICCV '23*) **\[**[**Paper**](https://arxiv.org/abs/2307.14039)**]**
* [ ] RAIRNet: Region-Aware Identity Rectification for Face Forgery Detection *(MM '23)* **\[**[**Paper**](https://dl.acm.org/doi/10.1145/3581783.3612321)**]**
* [ ] Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation (*ICCV '23*) **\[**[**Paper**](https://arxiv.org/abs/2307.08448)**]** **\[**[**Code**](https://github.com/EnVision-Research/Selective-Diffusion-Distillation)**]**
* [ ] UCF: Uncovering Common Features for Generalizable Deepfake Detection *(ICCV '23)* **\[**[**Paper**](https://arxiv.org/abs/2304.13949)**]**
* [ ] Learning Patch-Channel Correspondence for Interpretable Face Forgery Detection (*TIP '23*) **\[**[**Paper**](https://doi.org/10.1109/TIP.2023.3246793)**]** **\[**[**Code**](https://github.com/Jae35/IFFD)**]**
* [ ] Contrastive Multi-FaceForensics: An End-to-end Bi-grained Contrastive Learning Approach for Multi-face Forgery Detection *(arXiv '23')* **\[**[**Paper**](https://arxiv.org/abs/2308.01520v1)**]**
* [ ] Two-in-one Knowledge Distillation for Efficient Facial Forgery Detection *(arXiv '23')* **\[**[**Paper**](https://arxiv.org/abs/2302.10437)**]**
* [ ] AUNet: Learning Relations Between Action Units for Face Forgery Detection (*CVPR '23*) **\[**[**Paper**](http://openaccess.thecvf.com/content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html)**]**
* [ ] AltFreezing for More General Face Forgery Detection (*CVPR '23*) **\[**[**Paper**](http://openaccess.thecvf.com/content/CVPR2023/html/Wang_AltFreezing_for_More_General_Video_Face_Forgery_Detection_CVPR_2023_paper.html)**]** \*\*\[[Code](https://github.com/ZhendongWang6/AltFreezing)]88
* [ ] $F^2$Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection (*TIFS '23*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/10004978)**]**
* [ ] On the Security of the One-and-a-Half-Class Classifier for SPAM Feature-Based Image Forensics (*TIFS '23*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/10098583)**]**
* [ ] Multimodaltrace: Deepfake Detection Using Audiovisual Representation Learning (*CVPRW '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/CVPR2023W/WMF/html/Raza_Multimodaltrace_Deepfake_Detection_Using_Audiovisual_Representation_Learning_CVPRW_2023_paper.html)**]**
* [x] [MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection](https://paper.imzh.me/face-forgery/mc-lcr) (*KBS '22*) **\[**[**Paper**](https://arxiv.org/abs/2110.03290)**]**
* [x] [Multi-Scale Wavelet Transformer for Face Forgery Detection](https://paper.imzh.me/face-forgery/multi-scale-wavelettransformer) (*ACCV '22*) **\[**[**Paper**](https://arxiv.org/abs/2210.03899)**]**
* [ ] Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection (*CVPR '22*) **\[**[**Paper**](https://arxiv.org/abs/2201.07131)**]** **\[**[**Code**](https://github.com/ahaliassos/RealForensics)**]**
* [ ] SSTNet: Detecting Manipulated Faces Through Spatial, Steganalysis and Temporal Features (*ICASSP '20*) **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/9053969/)**]**
* [x] Portrait shadow manipulation (*ACM MM / TOG '20*) **\[**[**Paper**](https://arxiv.org/abs/2005.08925)**]** **\[**[**Code**](https://github.com/google/portrait-shadow-manipulation)**]**
### Copy Move
**复制移动篡改定位**问题
* [ ] MGCFDN: Image copy-move forgery detection method based on multi-granularity feature consistency [](https://www.sciencedirect.com/science/article/pii/S0925231225027018) [](https://github.com/tuhanglsWorld/MGCFDN)
* [ ] DSTNet: Distinguishing Source and Target Areas for Image Copy-Move Forgery Detection [](https://link.springer.com/chapter/10.1007/978-3-031-78312-8_21)
* [ ] A Two-Phase Scheme by Integration of Deep and Corner Feature for Balanced Copy-Move Forgery Localization [](https://ieeexplore.ieee.org/document/10738218/)
* [ ] DCM-Net: A Diffusion Model-Based Detection Network Integrating the Characteristics of Copy-Move Forgery [](https://ieeexplore.ieee.org/document/10814718/)
* [ ] A Copy-move Forgery Detection Network based on Selective Sampling Attention and Low-cost Two-step Self-correlation Calculation [](https://ieeexplore.ieee.org/abstract/document/10856558/)
* [ ] TransCMFD: An adaptive transformer for copy-move forgery detection [](https://doi.org/10.1016/j.neucom.2025.130110)
* [ ] Copy-move Forgery Image Detection based on Cross-Scale Modeling and Alternating Refinement [](https://ieeexplore.ieee.org/abstract/document/10891504/)
* [ ] Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset [](https://arxiv.org/abs/2412.10258) [](https://github.com/YoursEver/FakeParaEgg)
* [ ] Copy-Move Forgery Detection and Question Answering for Remote Sensing Image [](https://arxiv.org/abs/2412.02575) [](https://github.com/shenyedepisa/RSCMQA)
* [ ] CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection [](https://doi.org/10.1109/TMM.2023.3345160)
* [ ] Robust Image Hashing via CP Decomposition and DCT for Copy Detection (*TOMM '24*) **\[**[**Paper**](https://dl.acm.org/doi/full/10.1145/3650112)**]**
* [ ] Lightweight and High-Precision Network for Image Copy-Move Forgery Detection (*SPL '24*) **\[**[**Paper**](https://doi.org/10.1109/LSP.2024.3400055)**]**
* [ ] Advancing Copy-Move Manipulation Detection in Complex Image Scenarios Through Multiscale Detector (*IEEE Access ‘24*) **\[**[**Paper**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10521517)**]**
* [ ] Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection (*TOMM '24*) **\[**[**Paper**](https://dl.acm.org/doi/abs/10.1145/3669905)**]**
* [ ] Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid Missed Detection and False Alarm *(arXiv '24)* **\[**[**Paper**](https://arxiv.org/abs/2406.03271v1)**]** **\[**[**Code**](https://github.com/LUZW1998/CMFDL)**]**
* [ ] Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning [](https://arxiv.org/abs/2404.17310) *(*~~*arXiv '24*~~*)* **\[**[**Paper**](https://arxiv.org/abs/2404.17310)**]**
* [ ] Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining (*WWW '24*) **\[**[**Paper**](https://arxiv.org/abs/2404.00611)**]**
* [ ] UCM-Net: A U-Net-Like Tampered-Region-Related Framework for Copy-Move Forgery Detection (*TMM '24*) **\[**[**Paper**](https://ieeexplore.ieee.org/document/10109179)**]**
* [ ] An effective image copy-move forgery detection using entropy image *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2312.11793)**]**
* [ ] CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2311.13263)**]**
* [ ] An approach for copy-move image multiple forgery detection based on an optimized pre-trained deep learning model *(KBS '23)* **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0950705123002587)**]**
* [ ] Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch (*ICME '23*) **\[**[**Paper**](https://arxiv.org/abs/2308.04188)**]**
* [x] [BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization](https://paper.imzh.me/copy-move/busternet) (*ECCV '18*) **\[**[**Paper**](http://openaccess.thecvf.com/content_ECCV_2018/html/Rex_Yue_Wu_BusterNet_Detecting_Copy-Move_ECCV_2018_paper.html)**]** **\[**[**Code**](https://github.com/isi-vista/BusterNet)**]**
* [x] [A Serial Image Copy-Move Forgery Localization Scheme With Source/Target Distinguishment](https://paper.imzh.me/copy-move/cmsdnet) (*TMM '20*) **\[**[**Paper**](https://ieeexplore.ieee.org/abstract/document/9207851/)**]** **\[**[**Code**](https://github.com/imagecbj/A-serial-image-copy-move-forgery-localization-scheme-with-source-target-distinguishment)**]**
* [x] [DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization](https://paper.imzh.me/copy-move/doa-gan) (*CVPR '20*) **\[**[**Paper**](http://openaccess.thecvf.com/content_CVPR_2020/html/Islam_DOA-GAN_Dual-Order_Attentive_Generative_Adversarial_Network_for_Image_Copy-Move_Forgery_CVPR_2020_paper.html)**]** **\[**[**Code**](https://github.com/asrafulashiq/doagan_clean)**]**
* [x] [Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue](https://paper.imzh.me/copy-move/selfdm-ps) (*TIP '22*) **\[**[**Paper**](https://arxiv.org/abs/2012.08697)**]**
* [ ] QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method (*Neurocomputing '22*) **\[**[**Paper**](https://www.sciencedirect.com/science/article/pii/S0925231222011031)**]** **\[**[**Code**](https://github.com/MehradAria/QDL-CMFD)**]**
* [ ] [Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection](https://paper.imzh.me/copy-move/word2phrasecmfd) *(TIFS '23)* **\[**[**Paper**](https://arxiv.org/abs/2207.09135)**]** **\[**[**Code**](https://github.com/ChaoWang1016/word2phraseCMFD)**]**
### Image Inpainting
* [ ] Dense Feature Interaction Network for Image Inpainting Localization [](https://arxiv.org/abs/2408.02191) [](https://github.com/Boombb/DeFI-Net_Inpainting)
* [ ] Image Inpainting Detection via Dual Guidance of Uncertainty and Precise Boundary Information [](https://ieeexplore.ieee.org/abstract/document/11005519)
* [ ] COCO-Inpaint: A Benchmark for Image Inpainting Detection and Manipulation Localization [](https://arxiv.org/abs/2504.18361)
* [ ] A Large-scale AI-generated Image Inpainting Benchmark [](https://arxiv.org/abs/2502.06593) [](https://github.com/mever-team/DiQuID)
* [ ] InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models [](https://arxiv.org/abs/2501.02816) [](https://github.com/QixianHao/Inpaint32K_dataset)
* [ ] Wavelet based inpainting detection [](http://arxiv.org/abs/2408.06429)
* [ ] Enhanced Wavelet Scattering Network for image inpainting detection [](http://arxiv.org/abs/2409.17023) [](https://github.com/jmaba/Wavelet-based-inpainting-detection)
### Tamper Text in Detection
图像中的**文本篡改检测**问题 (parts of)
* [ ] RealDTT: Towards a Comprehensive Real-World Dataset for Tampered Text Detection [](https://link.springer.com/article/10.1007/s11263-025-02515-2) [](https://github.com/edmundhaohao/RealDTT)
* [ ] Revisiting Tampered Scene Text Detection in the Era of Generative AI [](https://arxiv.org/abs/2407.21422) [](https://github.com/qcf-568/OSTF)
* [ ] ADCD-Net: Robust Document Image Forgery Localization via Adaptive DCT Feature and Hierarchical Content Disentanglement [](https://arxiv.org/abs/2507.16397) [](https://github.com/KAHIMWONG/ADCD-Net)
* [ ] FantasyID: A dataset for detecting digital manipulations of ID-documents [](https://arxiv.org/abs/2507.20808) [Dataset](https://www.idiap.ch/paper/fantasyid)
* [ ] Towards Generalized Physical Occlusion Detection On Documents [](https://paper.imzh.me/readme)
* [ ] DITL²: Dual-Stage Invariance Transfer Learning for Generalizable Document Image Tampering Localization [](https://paper.imzh.me/readme)
* [ ] Document image forgery detection and localization in desensitization scenarios [](https://www.sciencedirect.com/science/article/pii/S0165168425002373)
* [ ] Handwritten Signature Verification via Multimodal Consistency Learning [](https://ieeexplore.ieee.org/document/10950350/)
* [ ] Cross-Attention Based Two-Branch Networks for Document Image Forgery Localization in the Metaverse [](https://doi.org/10.1145/3686158)
* [ ] Explainable Tampered Text Detection via Multimodal Large Models [](http://arxiv.org/abs/2412.14816)
* [ ] Enhancing Tampered Text Detection through Frequency Feature Fusion and Decomposition [](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/04834.pdf)
* [ ] Delving into Adversarial Robustness on Document Tampering Localization [](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/08224.pdf)
* [ ] Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning [](https://arxiv.org/abs/2408.09676)
* [ ] Generalized Tampered Scene Text Detection in the era of Generative AI [](https://arxiv.org/abs/2407.21422)
* [ ] A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition *(arXiv '24)* **\[**[**Paper**](https://arxiv.org/abs/2402.13545)**]**
* [ ] CTP-Net: Character Texture Perception Network for Document Image Forgery Localization (*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2308.02158v1)**]** [](https://github.com/FCTMdataset/FCTM)
* [ ] Toward Real Text Manipulation Detection: New Dataset and New Solution *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2312.06934)**]** **\[**[**Code**](https://github.com/DrLuo/RTM)**]** [](https://ieeexplore.ieee.org/abstract/document/10883001/) *❗Updated*
* [ ] Towards Robust Tampered Text Detection in Document Image: New dataset and New Solution (*CVPR '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/CVPR2023/papers/Qu_Towards_Robust_Tampered_Text_Detection_in_Document_Image_New_Dataset_CVPR_2023_paper.pdf)**]** **\[**[**Code**](https://github.com/qcf-568/DocTamper)**]**
* [ ] Progressive Supervision for Tampering Localization in Document Images (*ICONIP '23*) **\[**[**Paper**](https://link.springer.com/chapter/10.1007/978-981-99-8184-7_11)**]**
* [ ] SigScatNet: A Siamese + Scattering based Deep Learning Approach for Signature Forgery Detection and Similarity Assessment *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/pdf/2311.05579.pdf)**]**
* [ ] Image Generation and Learning Strategy for Deep Document Forgery Detection *(arXiv '23)* **\[**[**Paper**](https://arxiv.org/abs/2311.03650)**]**
* [ ] Forgery-free signature verification with stroke-aware cycle-consistent generative adversarial network *(Neurocomputing '22)* **\[**[**Paper**](https://doi.org/10.1016/j.neucom.2022.08.017)**]** **\[**[**Code**](https://github.com/KAKAFEI123/Stroke-cCycleGAN)**]**
* [ ] Document Forgery Detection in the Context of Double JPEG Compression *(ICPR '22)* **\[**[**Paper**](https://link.springer.com/chapter/10.1007/978-3-031-37745-7_5)**]**
### Low Level Vision
Related resources:
*
*
Low-level tasks include super-resolution, denoise, dehze, low-light enhancement, etc. High-level tasks include classification, detection, segmentation, etc. segmentation, and so on. However, the ones I have listed here are probably still mainly related to tampering detection.
> Testing the new layout of paper title.
>
> 📖Paper, 👨💻Code, 📦Dataset, 🔗Other links, 📜News,
>
> \*Equal contribution. #Corresponding author.
* [ ] Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models [](https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Q-Instruct_Improving_Low-level_Visual_Abilities_for_Multi-modality_Foundation_Models_CVPR_2024_paper.html) [](https://github.com/Q-Future/Q-Instruct/)
* [ ] (**EVP**) Explicit Visual Prompting for Low-Level Structure Segmentations (*CVPR '23*) [📖](https://arxiv.org/abs/2303.10883), [👨💻](https://github.com/NiFangBaAGe/Explicit-Visual-Prompt) (*including defocus blur, shadow, forgery, camouflaged dection*)
> [Weihuang Liu](https://github.com/nifangbaage)1, [Xi Shen](https://xishen0220.github.io/)2, [Chi-Man Pun](https://www.cis.um.edu.mo/~cmpun/)#,1, [Xiaodong Cun](https://vinthony.github.io/)#,2
>
> 1University of Macau 2Tencent AI Lab
* [ ] SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device (*ICCV '23*) [📖](https://arxiv.org/abs/2308.08137), [👨💻](https://github.com/sanechips-multimedia/syenet)
> [Weiran Gou](https://github.com/WeiranGou)∗1,2, Ziyao Yi∗1,2, Yan Xiang1,2, Shaoqing Li1,2, Zibin Liu1,2, Dehui Kong1,2, Ke Xu#1,2
>
> 1State Key Laboratory of Mobile Network and Mobile Multimedia Technology, 2Sanechips Technology, Chengdu, China
* [ ] Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision (*ICLR* '24\_) [📖](https://arxiv.org/abs/2309.14181), [👨💻](https://github.com/VQAssessment/Q-Bench)
> [Haoning Wu](https://teowu.github.io/)1\*, [Zicheng Zhang](https://github.com/zzc-1998)2\*, [Erli Zhang](https://github.com/ZhangErliCarl/)1\*, [Chaofeng Chen](https://chaofengc.github.io/)1, [Liang Liao](https://liaoliang92.github.io/)1, [Annan Wang](https://github.com/AnnanWangDaniel)1, [Chunyi Li](https://github.com/lcysyzxdxc)2, [Wenxiu Sun](https://wenxiusun.com/)3, [Qiong Yan](https://scholar.google.com/citations?user=uT9CtPYAAAAJ\&hl=en)3, [Guangtao Zhai](https://ee.sjtu.edu.cn/en/FacultyDetail.aspx?id=24\&infoid=153\&flag=153)2, [Weisi Lin](https://personal.ntu.edu.sg/wslin/Home.html)1#
>
> 1Nanyang Technological University, 2Shanghai Jiaotong University, 3Sensetime Research
### Image Matching
**特征匹配**，图像匹配问题。
* [ ] FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer(*arXiv '23*) **\[**[**Paper**](https://arxiv.org/abs/2310.13605)**]**
* [x] [LoFTR: Detector-Free Local Matching with Transformers](https://paper.imzh.me/image-matching/loftr) (*CVPR '21*) **\[**[**Paper**](https://arxiv.org/abs/2104.00680)**]** **\[**[**Code**](https://github.com/zju3dv/LoFTR)**]**
* [ ] PATS: Patch Area Transportation with Subdivision for Local Feature Matching (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.07700)**]** **\[**[**Code**](https://github.com/zju3dv/pats)**]** **\[**[**Project**](https://zju3dv.github.io/pats/)**]**
* [ ] Adaptive Spot-Guided Transformer for Consistent Local Feature Matching (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.16624)**]** **\[**[**Code**](https://github.com/ASTR2023/ASTR)**]** **\[**[**Project**](https://astr2023.github.io/)**]**
* [ ] ObjectMatch: Robust Registration using Canonical Object Correspondences (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2212.01985)**]** **\[**[**Code**](https://github.com/cangumeli/ObjectMatch)**]** **\[**[**Project**](https://cangumeli.github.io/ObjectMatch/)**]**
### Object Detection
**目标检测**，包括伪装物体目标检测和突出目标检测，COD以及SOD。
* [ ] HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping *(AAAI '24)* **\[**[**Paper**](https://arxiv.org/abs/2312.17492)**]**
* [ ] Endow SAM with Keen Eyes: Temporal-spatial Prompt Learning for Video Camouflaged Object Detection *(CVPR '24)*
* [ ] VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning *(CVPR '24)* **\[**[**Paper**](https://arxiv.org/abs/2311.15011)**]**
* [ ] Weakly Supervised Open-Vocabulary Object Detection *(AAAI '24)* **\[**[**Paper**](https://arxiv.org/abs/2312.12437)**]**
* [ ] OTA: Optimal Transport Assignment for Object Detection (*CVPR '21*) **\[**[**Paper**](https://arxiv.org/abs/2103.14259)**]** **\[**[**Code**](https://github.com/Megvii-BaseDetection/OTA)**]**
* [ ] Consistency-basd Active Learning for Object Detection (*CVPRW '22*) **\[**[**Paper**](http://128.84.21.203/abs/2103.10374)**]** **\[**[**Code**](https://github.com/we1pingyu/CALD)**]**
* [ ] [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://paper.imzh.me/object-dection/zoomnet-cod) (*CVPR '22*) **\[**[**Paper**](https://arxiv.org/abs/2203.02688)**]** **\[**[**Code**](https://github.com/lartpang/ZoomNet)**]**
* [ ] Unsupervised Object Localization: Observing the Background to Discover Objects (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2212.07834)**]** **\[**[**Code**](https://github.com/valeoai/FOUND)**]**
* [ ] Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction (*CVPR '23*) **\[**[**Paper**](https://openreview.net/pdf?id=lin5jPqCQ6)**]** **\[**[**Code**](https://github.com/ChunmingHe/FEDER)**]**
* [ ] Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.14816)**]** **\[**[**Code**](https://github.com/ZhouHuang23/FSPNet)**]**
* [ ] Locate, Refine and Restore: A Progressive Enhancement Network for Camouflaged Object Detection (*IJCAI '23*) **\[**[**Paper**](https://www.ijcai.org/proceedings/2023/0124.pdf)**]**
* [ ] Spatial-Aware Token for Weakly Supervised Object Localization (*ICCV '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.10438)**]** **\[**[**Code**](https://github.com/wpy1999/SAT)**]**
* [ ] Generative Prompt Model for Weakly Supervised Object Localization (*ICCV '23*) **\[**[**Paper**](https://arxiv.org/abs/2307.09756)**]** **\[**[**Code**](https://github.com/callsys/GenPromp)**]**
* [ ] Category-aware Allocation Transformer for Weakly Supervised Object Localization (*ICCV '23*) **\[**[**Paper**](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_Category-aware_Allocation_Transformer_for_Weakly_Supervised_Object_Localization_ICCV_2023_paper.html)**]**
### Semantic Segmentation
语义分割，将图片中完整语义（具有标签或者类别）的部分分割出来。不仅要进行目标检测检测到图像中的物体，还需要对每个像素分类。
* [ ] [Generative Semantic Segmentation](https://paper.imzh.me/semantic-segmentation/gss) (*CVPR '23*) **\[**[**Paper**](https://arxiv.org/abs/2303.11316)**]** **\[**[**Code**](https://github.com/fudan-zvg/GSS)**]**
* [ ] EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation **\[**[**Paper**](https://arxiv.org/abs/2205.14756)**]** **\[**[**Code**](https://github.com/mit-han-lab/efficientvit)**]**
* [ ] CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation **\[**[**Paper**](https://arxiv.org/abs/2303.11797)**]** **\[**[**Code**](https://github.com/KU-CVLAB/CAT-Seg)**]** **\[**[**Project**](https://ku-cvlab.github.io/CAT-Seg/)**]** **\[**[**Note\_community**](https://blog.csdn.net/P_LarT/article/details/131083586)\*\*]
### Anomaly Detection
异常检测，通常用于发现与正常模式或预期模式不符的图像与视频。
* [ ] Contextual Affinity Distillation for Image Anomaly Detection *(WACV '24)* **\[**[**Paper**](https://arxiv.org/abs/2307.03101)**]**
* [ ] PromptAD: Zero-Shot Anomaly Detection Using Text Prompts *(WACV '24)* **\[**[**Paper**](https://openaccess.thecvf.com/content/WACV2024/html/Li_PromptAD_Zero-Shot_Anomaly_Detection_Using_Text_Prompts_WACV_2024_paper.html)**]**
* [ ] Holistic Representation Learning for Multitask Trajectory Anomaly Detection *(WACV '24)* **\[**[**Paper**](https://arxiv.org/abs/2311.01851)**]** **\[**[**Code**](https://alexandrosstergiou.github.io/project_pages/TrajREC/index.html)**]**
### Image Steganography
* [ ] Finding Incompatible Blocks for Reliable JPEG Steganalysis [](https://arxiv.org/abs/2402.13660)
* [ ] LiDiNet: A Lightweight Deep Invertible Network for Image-in-Image Steganography [](https://doi.org/10.1109/TIFS.2024.3463547)
### Useful Links
1. CVPR 2025 Accepted Papers
2. ECCV 2024 Accepted papers
3. IJCAI 2024 Main Track Accepted Papers
4. ICLR 2024 Papers List
5. WACV 2024 Papers
6. MM 2023 Proceedings
7. ICML 2023
8. ICCV 2023 Paper List
9. AAAI 2023
10. SIGGRAPH *unofficial* eg SIGGRAPH 2023
11. TIFS
12. [More...](https://paper.imzh.me/related/papersource)
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