arXiv AI
Jul 21

Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs

arXiv:2607. 18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts.

By Yi Tang, Xinyi Shang, Jiacheng Cui, Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tran Dinh Tien, Ahmed Elhagry, Salwa K. Al Khatib, Tianjun Yao, Yonina C. Eldar, Jing-Hao Xue, Hao Li, Salman Khan, Zhiqiang Shen
arXiv Computer Vision
4d ago

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

FUSED is a new framework that jointly detects and localizes AI-generated inpainting by combining low-level forensic cues with high-level semantic features through a sparsely-gated Mixture-of-Experts architecture. It predicts both an image-level manipulation score and a pixel-level mask of the inpainted region. On the OpenSDID cross-generator benchmark, FUSED outperforms existing methods, especially on unseen generators, and transfers effectively to the AutoSplice and CocoGlide benchmarks, doubling localization performance.

By Anton Nuzhdin, Marcel Worring, Ivona Najdenkoska
arXiv Computer Vision
1d ago

TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors

The paper introduces TIGA, a source‑image‑free, training‑free attack that injects adversarial properties into a diffusion model’s sampling trajectory to evade black‑box AIGC forensic detectors. TIGA aggregates gradients from white‑box surrogate detectors to create a transferable prior, then uses anisotropic directional search with finite‑difference queries to estimate and stabilize directions for the DDIM trajectory, applying frequency‑domain reshaping to reduce artifacts. Experiments demonstrate strong black‑box attack performance, transferability, and robustness to post‑processing while maintaining high perceptual quality.

By Xia Du, Zhuosen Bao, Zheng Lin, Jizhe Zhou, Chi-man Pun, Jun Luo, Symeon Chatzinotas
arXiv AI
Jun 2

Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization

arXiv:2606. 02178v1 Announce Type: cross Abstract: Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery localization methods, as these approaches depend on physical noise absent in synthetic data.

By Yiming Wang, Baiqi Wu, Qingming Li, Jiahao Chen, Tong Zhang, Shouling Ji