AI 中文总结
针对深度伪造检测中分布偏移下预测过自信的问题，本文提出含IBDC机制的多分支框架，结合CLIP等多视图结构学习，在跨数据集基准上实现最优泛化并提升校准度。
AI 中文摘要
安全关键的生物识别与取证应用需要准确的预测结果和可靠的置信度估计，尤其是在分布偏移的场景下。这一挑战在深度伪造检测领域尤为突出：基于基础模型的检测器往往对分布外的伪造操作表现出过度自信的预测，限制了其在实际部署中的适用性。本文提出一种不确定性感知的深度伪造检测框架，通过互补证据源之间的不一致性来识别伪造操作。该框架整合了三个分支：基于适配后的CLIP编码器的视觉分支、通过可微约束建模面部属性间一致性的语义分支，以及捕捉语义与取证特征间类别依赖模式的结构分支。为有效融合这些信号，本文引入了分支间分歧校准（IBDC）机制，这是一种感知分歧的不确定性建模方法，将预测不确定性与证据分支间的冲突关联起来。以FaceForensics++作为训练源的广泛跨数据集实验表明，所提框架在多个分布外基准上实现了最先进的泛化性能，同时持续提升了校准度与选择性预测表现。这些结果表明，结合感知分歧的互补证据与不确定性，为分布偏移下可靠且校准良好的深度伪造检测提供了坚实基础。
英文摘要
Security-critical biometric and forensic applications require accurate predictions and reliable confidence estimates, particularly under distribution shift. This challenge is especially acute for deepfake detection, where foundation-model-based detectors often exhibit overconfident predictions on out-of-distribution manipulations, which limits their suitability for operational deployment. We propose an uncertainty-aware deepfake detection framework that identifies manipulations through inconsistencies across complementary evidence sources. The framework integrates three streams: a visual stream based on an adapted CLIP encoder, a semantic stream that models consistency among facial attributes through differentiable constraints, and a structural stream that captures class-dependent dependency patterns between semantic and forensic features. To effectively combine these signals, we introduce Inter-Branch Disagreement Calibration (IBDC), a disagreement-aware uncertainty modeling mechanism that links predictive uncertainty to conflicts among evidence streams. Extensive cross-dataset experiments using FaceForensics++ as the training source demonstrate that the proposed framework achieves state-of-the-art generalization across multiple out-of-distribution benchmarks while consistently improving calibration and selective prediction performance. These results show that combining complementary evidence with disagreement-aware uncertainty provides a robust foundation for trustworthy and well-calibrated deepfake detection under distribution shift.
