Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow

This paper proposes Sparse-Constraint Rectified Flow (SC-RF), a generative detector for open-set visual-text forensics. Instead of learning boundaries tied to known forgery patterns, it localizes manipulation through the estimated local restoration cost needed to align an image with authentic visual-text statistics. The system combines self-supervised Artifact Injection with a pixel-space Forensic-DiT intended to preserve high-frequency traces. According to the supplied abstract, experiments on three benchmarks outperform the runner-up by 3.2 F1 points and 4.8 IoU points, with strong zero-shot results on unseen editing patterns.