TriDF：面向可解释深度伪造检测的感知、检测与幻觉评测基准
TriDF: Evaluating Perception, Detection, and Hallucination for Interpretable DeepFake Detection
把「感知证据定位—分类检测—解释可靠性」三者放进同一评测框架，并给出人工标注的细粒度伪造证据，可直接用于诊断多模态大模型在伪造检测中「看对了才判对」的因果链，也为设计带证据输出的检测器提供监督信号与评价协议。
原文依据 TriDF contains high-quality forgeries from advanced synthesis models, covering 16 DeepFake types across image, video, and audio modalities.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论生成建模的进展使伪造真实人物形象变得愈发容易，给安全、传播与公众信任带来严重风险。检测此类以人物为对象的篡改，要求系统不仅能区分被改动的内容与真实媒体，还能给出清晰可靠的推理。本文提出 TriDF，一个面向可解释深度伪造检测的综合基准。TriDF 包含来自先进合成模型的高质量伪造样本，覆盖图像、视频与音频三种模态下的 16 种深度伪造类型。该基准从三个关键方面进行评估：感知，利用人工标注的证据衡量模型识别细粒度篡改痕迹的能力；检测，评估模型在不同伪造类别与生成器上的分类性能；幻觉，量化模型所生成解释的可靠性。对最优多模态大语言模型的实验表明，准确的感知对于可靠检测至关重要，但幻觉会严重干扰决策，揭示出这三个方面之间的相互依赖关系。TriDF 提供了一个统一框架，用于理解检测准确率、证据识别与解释可靠性之间的相互作用，为构建应对真实世界合成媒体威胁的可信系统奠定基础。
英文原文摘要 1447 字符
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.