TextSleuth: Towards Explainable Tampered Text Detection

ACL ARR 2025 February Submission7838 Authors

Abstract: Recent advancements in tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the detection lacks convincing interpretation and clarity, making the prediction unreliable. To address this problem, we propose to explain the basis of tampered text detection with natural language via large multimodal models. To bridge the data gap, we propose a large-scale, comprehensive dataset, ETTD, which contains both pixel-level annotations for tampered text region and natural language annotations describing the anomaly of the tampered text. Multiple methods are employed to improve the quality of our dataset, such as using elaborated queries to generate high-quality anomaly descriptions with GPT-4o. A fused mask prompt is proposed to reduce confusion when querying GPT-4o to generate anomaly descriptions. To automatically filter out low-quality annotations, we also propose to prompt GPT-4o to recognize tampered texts before describing the anomaly, and to filter out the responses with low OCR accuracy. To further improve explainable tampered text detection, we propose a simple yet effective model called TextSleuth, which improves fine-grained perception and cross-domain generalization by focusing on the suspected region, with a two-stage analysis paradigm and an auxiliary grounding prompt. Extensive experiments on both the ETTD dataset and the public dataset have verified the effectiveness of the proposed methods. Our dataset and code will be made publicly available.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering, cross-modal application

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English, Chinese

Submission Number: 7838