TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection

Jian-Yu Jiang-Lin; Kang-Yang Huang; Ling Zou; Ling Lo; Sheng-Ping Yang; Yu-Wen Tseng; Kun-Hsiang Lin; Chia-Ling Chen; Yu-Ting Ta; Yan-Tsung Wang; Po-Ching Chen; Hongxia Xie; Hong-Han Shuai; Wen-Huang Cheng

arXiv · 2512.10652

TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection

Jian-Yu Jiang-Lin·Kang-Yang Huang·Ling Zou·Ling Lo·Sheng-Ping Yang·Yu-Wen Tseng·Kun-Hsiang Lin·Chia-Ling Chen·Yu-Ting Ta·Yan-Tsung Wang·Po-Ching Chen·Hongxia Xie·Hong-Han Shuai·Wen-Huang Cheng

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Abstract

Advances in generative modeling have made it increasingly easy to fabricate realistic portrayals of individuals, creating serious risks for security, communication, and public trust. Detecting such person-driven manipulations requires systems that not only distinguish altered content from authentic media but also provide clear and reliable reasoning. In this paper, we introduce TriDF, a comprehensive benchmark for interpretable DeepFake detection. TriDF contains high-quality forgeries from advanced synthesis models, covering 16 DeepFake types across image, video, and audio modalities. The benchmark evaluates three key aspects: Perception, which measures the ability of a model to identify fine-grained manipulation artifacts using human-annotated evidence; Detection, which assesses classification performance across diverse forgery families and generators; and Hallucination, which quantifies the reliability of model-generated explanations. Experiments on state-of-the-art multimodal large language models show that accurate perception is essential for reliable detection, but hallucination can severely disrupt decision-making, revealing the interdependence of these three aspects. TriDF provides a unified framework for understanding the interaction between detection accuracy, evidence identification, and explanation reliability, offering a foundation for building trustworthy systems that address real-world synthetic media threats.

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Jian-Yu Jiang-Lin, Kang-Yang Huang, Ling Zou, Ling Lo, Sheng-Ping Yang, Yu-Wen Tseng, Kun-Hsiang Lin, Chia-Ling Chen, Yu-Ting Ta, Yan-Tsung Wang, Po-Ching Chen, Hongxia Xie, Hong-Han Shuai, Wen-Huang Cheng. 2026-03-28. TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection. https://arxiv.org/abs/2512.10652

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