Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization

TL;DR

This paper introduces a novel RNN-based multi-modal attention framework that effectively detects and localizes audio-visual deepfakes by leveraging contextual information, outperforming existing methods on multiple datasets.

Contribution

The paper presents a new multi-modal attention approach using RNNs for improved audio-visual deepfake detection and localization, addressing the modality gap challenge.

Findings

Achieved 3.47% higher accuracy in deepfake detection

Improved localization precision by 2.05%

Demonstrated superior performance on multiple datasets

Abstract

In the digital age, the emergence of deepfakes and synthetic media presents a significant threat to societal and political integrity. Deepfakes based on multi-modal manipulation, such as audio-visual, are more realistic and pose a greater threat. Current multi-modal deepfake detectors are often based on the attention-based fusion of heterogeneous data streams from multiple modalities. However, the heterogeneous nature of the data (such as audio and visual signals) creates a distributional modality gap and poses a significant challenge in effective fusion and hence multi-modal deepfake detection. In this paper, we propose a novel multi-modal attention framework based on recurrent neural networks (RNNs) that leverages contextual information for audio-visual deepfake detection. The proposed approach applies attention to multi-modal multi-sequence representations and learns the contributing features among them for deepfake detection and localization. Thorough experimental validations on audio-visual deepfake datasets, namely FakeAVCeleb, AV-Deepfake1M, TVIL, and LAV-DF datasets, demonstrate the efficacy of our approach. Cross-comparison with the published studies demonstrates superior performance of our approach with an improved accuracy and precision by 3.47% and 2.05% in deepfake detection and localization, respectively. Thus, obtaining state-of-the-art performance. To facilitate reproducibility, the code and the datasets information is available at https://github.com/vcbsl/audiovisual-deepfake/.