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SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection

2026-08-06 02:52:49
Kahim Wong, Kemou Li, Yiming Chen, Haiwei Wu, Jiantao Zhou
arXiv_CV
           
Abstract

AI-assisted image editing threatens trust in financial, legal, and identity records. The GenText-Forensics Challenge at ACM MM 2026 addresses this by requiring structured forensic reports, in which integrating detection, pixel-level localization, and natural language explanation for multilingual text-centric forgery images. We present SEED, a modular system with three components. First, a similarity-guided pipeline augments training with diverse synthetic forgeries. Second, a single ViT, built on DINOv3 with LoRA adaptation, jointly performs detection and pixel-level localization while preserving pre-trained priors with minimal trainable parameters. Third, an evolving harness takes the detector's predictions and generates a complete forensic report via an MLLM, iteratively improved through a proposer-evaluator loop optimizing report quality. SEED ranked 3rd in the GenText-Forensics Challenge. Code and data are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2606.21138

PDF

https://arxiv.org/pdf/2606.21138.pdf

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