Audio-visual deepfake detection scrutinizes manipulations in public video
using complementary multimodal cues. Current methods, which train on fused
multimodal data for multimodal targets face challenges due to uncertainties and
inconsistencies in learned representations caused by independent modality
manipulations in deepfake videos. To address this, we propose cross-modality
and within-modality regularization to preserve modality distinctions during
multimodal representation learning. Our approach includes an audio-visual
transformer module for modality correspondence and a cross-modality
regularization module to align paired audio-visual signals, preserving modality
distinctions. Simultaneously, a within-modality regularization module refines
unimodal representations with modality-specific targets to retain
modal-specific details. Experimental results on the public audio-visual
dataset, FakeAVCeleb, demonstrate the effectiveness and competitiveness of our
approach.
Audio-visual deepfake detection scrutinizes manipulations in public video using complementary multimodal cues
Deepfake videos have become a significant challenge in today’s digital landscape, and detecting these manipulations is crucial to maintaining trust in multimedia information systems. This article presents a novel approach to deepfake detection that leverages multimodal cues, combining both audio and visual information.
The use of multimodal data for training deepfake detection models has posed challenges due to uncertainties and inconsistencies in the learned representations. This is primarily caused by independent manipulations in different modalities within deepfake videos. To address this problem, the proposed approach incorporates cross-modality and within-modality regularization techniques.
Cross-modality regularization
The cross-modality regularization module aims to preserve modality distinctions during multimodal representation learning. It achieves this by aligning paired audio-visual signals, ensuring that the audio and visual components correspond appropriately. This alignment helps in identifying any inconsistencies that may arise from deepfake manipulations.
Within-modality regularization
The within-modality regularization module focuses on refining unimodal representations with modality-specific targets. By doing so, it retains modal-specific details and further enhances the ability to identify any manipulations. This module fine-tunes the representations to capture the nuances specific to each modality, such as acoustic patterns in audio and visual features in videos.
The proposed approach also employs an audio-visual transformer module for modality correspondence. This module plays a crucial role in ensuring that the audio and visual information aligns correctly, enabling more accurate detection of deepfake manipulations.
Experimental results on the FakeAVCeleb dataset demonstrate the effectiveness and competitiveness of the proposed approach. The use of complementary multimodal cues and the incorporation of cross-modality and within-modality regularization techniques significantly enhance the ability to scrutinize manipulations in public video.
From a broader perspective, this research contributes to the field of multimedia information systems, specifically in the domain of deepfake detection. The cross-disciplinary nature of this work combines concepts from multimedia analysis, artificial reality, augmented reality, and virtual realities. By leveraging multimodal cues, this research presents a robust approach to detecting deepfakes, addressing the challenges posed by independent modality manipulations.
In conclusion, the proposed approach for audio-visual deepfake detection demonstrates the importance of considering multiple modalities in multimedia analysis. Through the use of cross-modality and within-modality regularization techniques, more accurate and robust deepfake detection can be achieved, contributing to the advancement of multimedia information systems and related fields.