发表机构
National Taiwan University; National Tsing Hua University; National Yang Ming Chiao Tung University（国立台湾大学; 国立清华大学; 国立阳明交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出无训练框架Look Before You Judge，通过对比原始与模糊图像的注意力差异挖掘局部证据区域，整合全局上下文进行可解释深度伪造检测，显著提升准确率并降低幻觉。
AI 中文摘要
多模态大语言模型（MLLMs）能够用自然语言解释深度伪造的判定结果，但这种解释未必在视觉上基于预测所依据的视觉证据。模型可能描述从语言先验中推断出的看似合理的伪影，而非来自图像证据。现有的接地方法通过解码或注意力干预来增强视觉依赖，但它们通常在整个图像上增强接地，这使得它们不适合处理那些细微、空间局部化且依赖图像的取证伪影。我们提出“先看后判”（Look Before You Judge），一个无训练框架，将可解释的深度伪造检测表述为顺序证据获取过程。该框架不是直接从整体视觉推理中预测图像真实性，而是首先通过对比原始图像与其高斯模糊版本之间的MLLM解码器到视觉注意力，来识别图像特定的候选证据区域。随后逐个检查这些识别出的区域，并在最终判定前将局部证据与全局图像上下文整合。该框架无需操作掩码、外部取证模型或参数更新，可直接应用于现成的MLLMs。在TriDF和MMTD-Set上的五个开源MLLMs中，我们的框架将检测准确率提高了最多12.8%，将CHAIR降低了最多33.4%，幻觉率降低了最多21.3%，并优于代表性的无训练解码和注意力方法。
英文摘要
Multimodal large language models (MLLMs) can explain deepfake verdicts in natural language, but such explanations are not necessarily visually grounded in the visual evidence underlying the prediction. A model may describe plausible artifacts inferred from language priors rather than from image evidence. Existing grounding methods improve visual reliance through decoding or attention interventions, but they generally strengthen grounding over the entire image, making them ill-suited for forensic artifacts that are subtle, spatially localized, and image-dependent. We propose Look Before You Judge, a training-free framework that formulates explainable deepfake detection as a sequential evidence acquisition process. Instead of directly predicting image authenticity from holistic visual reasoning, our framework first identifies image-specific candidate evidence regions by contrasting the MLLM's decoder-to-visual attention between an original image and its Gaussian-blurred counterpart. The identified regions are then inspected individually, and the resulting local evidence is integrated with the global image context before reaching a final verdict. The framework operates without manipulation masks, external forensic models, or parameter updates, making it directly applicable to off-the-shelf MLLMs. Across five open-source MLLMs on TriDF and MMTD-Set, our framework improves detection accuracy by up to 12.8%, reduces CHAIR by up to 33.4% and hallucination rate by up to 21.3%, and outperforms representative training-free decoding and attention methods.
