We introduce Forensim, an attention-based state-space framework for image forgery detection that jointly localizes both manipulated (target) and source regions. Unlike traditional approaches that rely solely on artifact cues to detect spliced or forged areas, Forensim is designed to capture duplication patterns crucial for understanding context. In scenarios such as protest imagery, detecting only the forged region, for example a duplicated act of violence inserted into a peaceful crowd, can mislead interpretation, highlighting the need for joint source-target localization. Forensim outputs three-class masks (pristine, source, target) and supports detection of both splicing and copy-move forgeries within a unified architecture. We propose a visual state-space model that leverages normalized attention maps to identify internal similarities, paired with a region-based block attention module to distinguish manipulated regions. This design enables end-to-end training and precise localization. Forensim achieves state-of-the-art performance on standard benchmarks. We also release CMFD-Anything, a new dataset addressing limitations of existing copy-move forgery datasets.
Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络空间科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳广东省智能信息处理重点实验室）
Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University（广东智能信息处理重点实验室、电子与信息工程学院）
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Mayachitra Inc（Mayachitra公司）；University of California, Riverside（加州大学河滨分校）；Naval Air Warfare Center Weapons Division（海军空战中心武器部）；Mayachitra Inc., Santa Barbara, California, USA（Mayachitra公司（加利福尼亚州圣巴巴拉））；University of California, Riverside, California, USA（加州大学河滨分校（加利福尼亚州））；Naval Air Warfare Center Weapons Division, China Lake, California, USA（海军空战中心武器部（加利福尼亚州中国湖））
Detection and Localization of Multiple Image Splicing Using MobileNet V1
Symbiosis Institute of Technology, Symbiosis International (Deemed University)（共生国际（ deemed）大学共生技术学院）；Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University)（共生国际（ deemed）大学共生应用人工智能中心）
Identification of splicing edges in tampered image based on Dichromatic Reflection Model
Civil aviation college, Shenyang Aerospace University（沈阳航空航天大学民航学院）；Criminal Investigation Police University of China（中国刑事警察学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）
Pixel-Inconsistency Modeling for Image Manipulation Localization
Nanyang Technology University（南洋理工大学）；School of Electrical and Electronic Engineering（电子电气工程学院）；Rapid-Rich Object Search (ROSE) Lab（快速富对象搜索实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；City University of Hong Kong（香港城市大学）；University of Campinas（坎皮纳斯大学）；Institute of Computing（计算机研究所）；Artificial Intelligence Laboratory (Recod.ai)（人工智能实验室（Recod.ai））
