Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline
Jingchun Lian, Lingyu Liu Xi'an Jiaotong University, Yaxiong Wang Hefei University of Technology, Yujiao Wu CSIRO, Lianwei Wu, Li Zhu Xi'an Jiaotong University, Zhedong Zheng University of Macau
Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulation. To address this, we introduce Forgery Attribution Report Generation, a new multimodal task designed to provide post-hoc forensic evidence for manipulated images. This task jointly localizes forged regions (“Where“) and generates natural language explanations grounded in the editing process (“Why“). This dual-focus approach goes beyond traditional binary forensics, providing a comprehensive, interpretable understanding of the manipulation. To enable research in this domain, we present Multi-Modal Tamper Tracing (MMTT), a large-scale dataset of 152,217 samples. Each sample features a process-derived ground-truth mask and a human-authored textual description, ensuring high annotation precision and linguistic richness. We further propose ForgeryTalker, a unified end-to-end baseline that integrates vision and language via a shared encoder and dual decoders for mask and text generation. Experiments show that ForgeryTalker achieves competitive performance on both subtasks, i.e., 59.3 CIDEr and 73.67 IoU, establishing a strong baseline for explainable multimedia forensics. Our dataset and code are available at: https://github.com/NattyLianJc/Generating-Attribution-Reports.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表