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cs.CV2026
Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization
Xuekang Zhu, Kaiwen Feng, Ruifeng Wang +10
Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask for a given image . In this wor…
cs.CV2026
Towards Generalized Image Manipulation Localization via Score-based Model
Yunfei Wang, Bo Du, Zhe Yang +4
With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for ensuring the integrity of digital…
cs.CV2026
Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction
Bo Du, Xiaochen Ma, Xuekang Zhu +8
Fake Image Detection (FID), aiming at unified detection across four image forensic subdomains, is critical in real-world forensic scenarios. Compared with ensemble approaches, mono…