用生成式方法检测文本伪造，能识别未知攻击模式。
Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow
- 通过估计修复成本定位篡改区域，而非学习特定伪造特征。
- 在三个基准上F1和IoU分别领先第二名3.2和4.8个百分点。
- 对未见过的文本编辑有强零样本能力，适合检测新型伪造。
快速发展的生成式AI使得视觉文本篡改日益复杂，现有检测器常因过拟合特定伪造模式而泛化能力差。为此，我们提出一种生成式检测器，通过估算将查询图像恢复为真实视觉文本统计所需的局部修复成本来定位篡改区域，而非学习特定伪造的判别边界。具体地，提出面向检测的稀疏约束修正流（SC-RF），基于流匹配实现空间稀疏异常定位。通过自监督伪影注入缓解数据稀缺问题，并采用像素空间取证迪特（Forensic-DiT）保留高频取证痕迹。在三个基准上的实验表明，该方法性能达当前最优，F1和IoU分别比次优方案高出3.2和4.8个百分点。尤其在难以识别的未知文本编辑模式下展现出强大零样本能力。此外，辅以应力测试分析表明，本模型生成的局部调和可削弱现有检测器依赖的统计线索，提供互补的漏洞分析视角。
原文摘要 · Abstract (English)
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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