DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline

2026-09-20

AI Summary

本文提出DFD-Lab，一个模块化音视频深度伪造检测管道，通过集成多种检测器和实验流程解决数据集适应、输入表示等问题。

Abstract

Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental conditions. We present DFD-Lab, a modular pipeline that separates these responsibilities while supporting shared training and evaluation workflows. We integrate three implementations: Xception-based maximum-logit fusion, ResNet with temporal LSTM fusion, and our AVFF reimplementation. Experiments cover external testing, degradation-based training augmentation and evaluation-time corruption. On a filtered subset of Deepfake-Eval-2024, models trained on FakeAVCeleb attain baseline AUROC values of 0.504, 0.538 and 0.458. JPEG50 training augmentation raises these to 0.691, 0.605 and 0.570, respectively, while all three accuracies decrease. These results illustrate why training interventions, evaluation corruptions and metric-dependent outcomes should remain distinct within a common pipeline. The contribution is the integration of audio-visual processing, interchangeable detectors and configurable experimental workflows, supported by empirical case studies. The findings highlight the challenge of cross-dataset detection and the complementary information provided by ranking and classification metrics.

Problem

• deepfake detection
• audio-visual processing
• dataset adaptation
• temporal input representation
• model interfaces

Innovation

• audio-visual deepfake detection
• modular pipeline
• shared training and evaluation workflows
• cross-dataset detection