TAENet: Two-branch Autoencoder Network for Interpretable Deepfake Detection

Abstract

Deepfake detection attracts increasingly attention due to serious security issues caused by facial manipulation techniques. Recently, deep learning-based detectors have achieved promising performance. However, these detectors suffer severe untrustworthy due to the lack of interpretability. Thus, it is essential to work on the interpretibility of deepfake detectors to improve the reliability and traceability of digital evidence. In this work, we propose a two-branch autoencoder network named TAENet for interpretable deepfake detection. TAENet is composed of Content Feature Disentanglement (CFD), Content Map Generation (CMG), and Classification. CFD extracts latent features of real and forged content with dual encoder and feature discriminator. CMG employs a Pixel-level Content Map Generation Loss (PCMGL) to guide the dual decoder in visualizing the latent representations of real and forged contents as real-map and fake-map. In classification module, the Auxiliary Classifier (AC) serves as map amplifier to improve the accuracy of real-map image extraction. Finally, the learned model decouples the input image into two maps that have the same size as the input, providing visualized evidence for deepfake detection. Extensive experiments demonstrate that TAENet can offer interpretability in deepfake detection without compromising accuracy.