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

• URL: http://arxiv.org/abs/2608.02258v1
• Date: Mon, 03 Aug 2026 14:03:27 GMT
• Title: Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow
• Abstract summary: Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors.<n>We propose a generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics.<n>Our method achieves state-of-the-art performance, surpassing the runner-up by 3.2 and 4.8 percentage points in F1 and IoU, respectively.
• Score: 11.434432702589868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapidly evolving Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors. Existing discriminative models often overfit specific forgery patterns, limiting their generalization to unseen, open-set attacks. To address this challenge, we propose a generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics, rather than by learning forgery-specific decision boundaries. Specifically, we introduce Sparse-Constraint Rectified Flow (SC-RF), a detector-oriented adaptation of Flow Matching for spatially sparse anomaly localization. We further mitigate data scarcity via self-supervised Artifact Injection and preserve high-frequency forensic traces using a pixel-space Forensic-DiT. Extensive experiments on three benchmarks show that our method achieves state-of-the-art performance, surpassing the runner-up by 3.2 and 4.8 percentage points in F1 and IoU, respectively. In particular, the proposed detector demonstrates strong zero-shot performance on challenging unseen text editing patterns. We further provide an auxiliary stress-test analysis showing that local harmonization produced by our model can weaken the statistical cues relied upon by existing detectors, offering a complementary vulnerability-analysis perspective.

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