Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection

TL;DR

This paper introduces a frequency-aware triple-branch network for deepfake detection, combining spatial and frequency features with mutual information-based loss functions to improve robustness and generalization.

Contribution

It proposes a novel triple-branch network that jointly captures diverse features and employs mathematically derived loss functions to enhance feature relevance and model robustness.

Findings

Achieves state-of-the-art performance on six benchmark datasets.

Effectively captures diverse forgery artifacts across frequency domains.

Improves generalization to various deepfake manipulations.

Abstract

Advanced deepfake technologies are blurring the lines between real and fake, presenting both revolutionary opportunities and alarming threats. While it unlocks novel applications in fields like entertainment and education, its malicious use has sparked urgent ethical and societal concerns ranging from identity theft to the dissemination of misinformation. To tackle these challenges, feature analysis using frequency features has emergedas a promising direction for deepfake detection. However, oneaspect that has been overlooked so far is that existing methodstend to concentrate on one or a few specific frequency domains,which risks overfitting to particular artifacts and significantlyundermines their robustness when facing diverse forgery patterns. Another underexplored aspect we observe is that different features often attend to the same forged region, resulting in redundant feature representations and limiting the diversity of the extracted clues. This may undermine the ability of a model to capture complementary information across different facets, thereby compromising its generalization capability to diverse manipulations. In this paper, we seek to tackle these challenges from two aspects: (1) we propose a triple-branch network that jointly captures spatial and frequency features by learning from both original image and image reconstructed by different frequency channels, and (2) we mathematically derive feature decoupling and fusion losses grounded in the mutual information theory, which enhances the model to focus on task-relevant features across the original image and the image reconstructed by different frequency channels. Extensive experiments on six large-scale benchmark datasets demonstrate that our method consistently achieves state-of-the-art performance. Our code is released at https://github.com/injooker/Unveiling Deepfake.