Image Forgery Localization

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Image forgery localization studies how to identify not just whether an image is altered, but which pixels or regions were changed. Methods look for forensic traces such as noise, compression and frequency anomalies, and increasingly combine them with pretrained vision models, segmentation systems and semantic reasoning. Training ranges from pixel-level masks to weak labels, synthetic edits, and watermarks, with benchmarks covering documents, faces, and AI-generated inpainting. A central challenge is generalizing to new editing models and real-world image conditions without mistaking semantic boundaries or compression artifacts for evidence of tampering.

Latest papers

79 papers10 papers in the last four weeks, up 67% on the four weeks before. 0.1% of all new papers.

RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization
Oct 17, 2026Kahim Wong, Kemou Li, Yiming Chen +2Image Forgery Localization Text Forgery Detection
State Key Laboratory of Internet of Things for Smart City, University of Macau · School of Information and Software Engineering, University of Electronic Science and Technology of China
DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization
Oct 15, 2026Simon Freitter, David Fischinger, Silvia Poletti +2Image Forgery Localization Feature Fusion
AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria
RLDF: A Region-Level Deepfake Dataset and Benchmark for Partial-Face Manipulation Detection
Oct 15, 2026Shukesh Reddy, Abhijit DasDeepfake Detection Image Forgery Localization
Machine Intelligence Group, Department of CS&IS Birla Institute of Technology and Science, Pilani, Hyderabad Campus, India
MSU Team at the Explainable Deepfake Detection Challenge 2026: Grounded Artifact Evidence for Deepfake Detection
Oct 7, 2026Artem Filippov, Aleksandr Gushchin, Kirill Koltsov +2Deepfake Detection Explainable Deepfake Detection
Lomonosov Moscow State University Moscow, Russia · MSU Institute for Artificial Intelligence Moscow, Russia
Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization
Oct 6, 2026Xuekang Zhu, Kaiwen Feng, Ruifeng Wang +10Image Forgery Localization
Sichuan University · Ant Group · The Hong Kong University of Science and Technology +4
GIFTBench: Diagnosing Generalization in Image Forgery Localization and Informing Model Design
Oct 1, 2026Baoke Dou, Ziye Wang, Hao Wang +5Image Forgery Localization Cross-Dataset Generalization
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Sep 30, 2026Zhiya Tan, Jing Huang, Changtao Miao +5Tool Use in VLMs Image Forgery Detection
CCDS Nanyang Technological University Singapore, Singapore · Ant Digital Technologies Ant Group Singapore, Singapore · Ant Digital Technologies Ant Group Hangzhou, China +3
Team MSU GenText-Forensics Challenge 2026 Technical Report
Sep 29, 2026Kirill Koltsov, Aleksandr Gushchin, Dmitriy Vatolin +1Image Forgery Detection Digital Forensics
Lomonosov Moscow State University Moscow, Russia · MSU Institute for Artificial Intelligence Moscow, Russia
Forensic-Aware Continual Adaptation for Image Forgery Localization
Sep 29, 2026Chenqi Kong, Song Xia, Anwei Luo +3Image Forgery Detection Vision Foundation Model Adaptation
School of Computing, National University of Singapore, Singapore. · ROSE Lab, School of EEE, Nanyang Technological University, Singapore. · School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang, China. +1
Less Supervision, Better Generalization: Weakly Supervised Fake Region Localization in Diffusion-Edited Images
Sep 29, 2026Junhee Lee, Donghyeon Jeon, Taeoh Kim +2Image Forgery Localization Weakly Supervised Learning
Kyung Hee University · NAVER Cloud
IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models
Sep 17, 2026Dasom Choi, Sangjun Moon, Hyeongchan Im +5Image Forgery Detection Large Vision-Language Models
Chungnam National University · Institute of Science Tokyo · Nara Institute of Science and Technology (NAIST)
ChatGPT Images 2.5 on Forgery Tasks: Testing Advertised Improvements Against Known Answers
Sep 12, 2026Ankit Raj, Yuxin Zhang, Kidus Zewde +7Image Editing Evaluation Image Forgery Detection
From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images
Sep 2, 2026Annalisa Gallina, Marco Fiorucci, Marco Brigo +2Image Forgery Detection Digital Image Forensics
University of Padova Padova, Italy
Evidence-Guided Detection, Localization and Explanation for Text-Centric Image Forensics
Sep 2, 2026Peifeng Liu, Bin Li, Qingsong Zhang +3Image Forgery Detection Digital Image Forensics
Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, Shenzhen University
SegWave: Wavelet-Driven Segmentation of Tampered Regions
Aug 31, 2026Siddhi Pravin Lipare, Vishesh Kumar, Akshay AgarwalImage Forgery Detection Digital Image Forensics
IIIT-Hyderabad, India · Trustworthy BiometraVision Lab, IISER-Bhopal, India
APT: Anchor-aligned Perturbations for Tamper Localization in Fully Regenerated Images
Aug 31, 2026Suhyeon Ha, Woo Jae Kim, Joonsung Jeon +2Digital Image Forensics Image Forgery Localization
KAIST
FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization
Aug 28, 2026Anton Nuzhdin, Marcel Worring, Ivona NajdenkoskaAI-Generated Image Detection Image Forgery Detection
Informatics Institute, University of Amsterdam Amsterdam, The Netherlands
GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization
Aug 21, 2026Haozhen Yan, Siyuan Shan, Zijian Yu +4Image Forgery Localization OOD Generalization
Shanghai Jiao Tong University · Ant Group · Shenzhen University
Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
Aug 20, 2026Edgar Gonzalez-FernandezImage Forgery Detection Digital Image Forensics
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, Aguascalientes, Mexico.
Frozen DINO Localizes Image Edits Without a Localizer
Aug 19, 2026Zane Kumar, Vishal Jain, Bernhard KainzImage Forgery Localization Digital Image Forensics
Department of Computing St Paul’s School London London, United Kingdom · Department of Computing Imperial College London London, United Kingdom · Computing / AIBE Imperial College London / FAU London, UK / Erlangen, Germany
PATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLMs
Aug 19, 2026Yaqi Li, Jielun Peng, Yabin Wang +2Explainable Deepfake Detection Digital Image Forensics
Harbin Institute of Technology
Bit-Level Triangular Content-Aware Permutation for Fragile Image Watermarking: Zero False Positive Rate, Single-Bit Sensitivity, and Arbitrary Dimension Support
Aug 14, 2026Zahra Ghoraeian, Mohammad-Reza Sadeghi, Samaneh MashhadiImage Forgery Detection Digital Image Forensics
Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran · School of Mathematics and Computer Science, Iran University of Science and Technology, Narmak, Tehran, 1684613114, Iran
Towards a satellite image manipulation and deepfake localization benchmark dataset
Aug 5, 2026Jacob Arndt, Debvrat Varshney, Philipe Dias +1Digital Image Forensics Image Forgery Localization
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow
Aug 3, 2026Jiangling Zhang, Shuxuan Gao, Zeyu Chen +2Image Forgery Detection Rectified Flow
VCIP & TMCC & DISSec College of Computer Science Nankai University Tianjin, China · School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan, China · VCIP & TMCC & DISSec College of Computer Science & College of Cryptology and Cyber Science Nankai University Tianjin, China
Progressive Decision-Making for Localizing Open-Ended AI-Generated Image Forgeries
Jul 31, 2026Jingyi Hou, Xiaoxia Chen, Leyu Zhou +2Image Segmentation Image Forgery Localization
School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
Can Vision-Language Models Reason about AI Edits in Images?
Jul 30, 2026Darsha Udayanga, Pin-Yu Chen, Payel Das +1Image Forgery Detection Image Forgery Localization
1Rensselaer Polytechnic Institute · 2IBM Research
FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking
Jul 29, 2026Muñoz-Haro Javier, Teruel Andres, Tolosana Ruben +4Image Forgery Detection Digital Image Forensics
School of Engineering, Universidad Autonoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
Jul 29, 2026Hongyang Wang, Yichen Shi, Hongrui Li +3CoT Reasoning Group Relative Policy Optimization
Shijiazhuang Tiedao University · Fudan University · Shanghai Jiao Tong University +2
BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization
Jul 28, 2026Bugra Alperen Uluirmak, Rifat KurbanImage Forgery Detection Digital Image Forensics
Erciyes University, Kayseri, Türkiye · Abdullah Gül University, Kayseri, Türkiye
LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection
Jul 28, 2026Can Wang, Yuhao Wang, Yushe Cao +2Multimodal Deepfake Detection Digital Image Forensics
The Hong Kong Polytechnic University, Hong Kong, China · University College London, London, United Kingdom · Tsinghua University, Beijing, China +2

Oct 17, 2026J/K move · Enter open · S save

RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization

Kahim Wong, Kemou Li, Yiming Chen, Haiwei Wu+1

State Key Laboratory of Internet of Things for Smart City, University of Macau · School of Information and Software Engineering, University of Electronic Science and Technology of China

Image Forgery Localization Text Forgery Detection Domain Generalization Parameter-Efficient Fine-Tuning

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As AI-assisted image editing becomes increasingly prevalent, Text-Centric Image Forgery Localization (TFL) is essential for protecting trust in financial and legal records. However, we observe that existing TFL detectors predominantly rely on Full-Parameter Fine-Tuning (FPFT) and fail to generalize to unseen forgery types because FPFT can induce low-rank feature collapse and overfit training artifacts. Meanwhile, prior detectors capture diverse forgery traces with a single unified representation, making subtle traces entangled and difficult to learn. To address these challenges, we propose RiSE, a Residual Subspace Expert model that preserves pre-trained priors by freezing principal SVD components and adapting only the residual subspaces with a ViT-only segmentor to avoid feature collapse. RiSE introduces forgery-aware residual experts, where each expert is trained independently on a training subset with a particular forgery type, and we show that cross-domain localization performance improves consistently as more experts are added. At inference, we introduce Latent Perturbation Confidence (LPC), which selects the most confident expert by analytically computing the latent-perturbed output. LPC enables efficient confidence estimation with a single forward pass, avoiding repeated forward passes on perturbed samples and improving expert selection. By training on synthetic forgery data, RiSE with 1 expert outperforms prior methods on real-world forgery images by 20.4% while reducing training steps by 10 times. Scaling the number of experts to 9 yields a 35.8% gain. RiSE remains effective with DCT feature, even when the majority of the parameters is frozen with RGB-only pretraining. Code: https://github.com/KahimWong/RiSE.