SAVe: Self-Supervised Audio-visual Deepfake Detection Exploiting Visual Artifacts and Audio-visual Misalignment
📰 ArXiv cs.AI
SAVe is a self-supervised audio-visual deepfake detection framework that exploits visual artifacts and audio-visual misalignment to detect deepfakes
Action Steps
- Learn from authentic videos without relying on curated synthetic forgeries
- Exploit visual artifacts and audio-visual misalignment for deepfake detection
- Train a self-supervised model to detect inconsistencies between audio and visual modalities
- Evaluate the model on unseen manipulations to test its scalability and robustness
Who Needs to Know This
AI engineers and researchers working on deepfake detection and multimodal analysis can benefit from SAVe, as it provides a robust and scalable solution for detecting subtle visual artifacts and cross-modal inconsistencies
Key Insight
💡 Self-supervised learning can be effective for deepfake detection, reducing dependence on curated synthetic forgeries and improving scalability and robustness
Share This
💡 Detect deepfakes with SAVe, a self-supervised audio-visual framework that exploits visual artifacts and audio-visual misalignment
Key Takeaways
SAVe is a self-supervised audio-visual deepfake detection framework that exploits visual artifacts and audio-visual misalignment to detect deepfakes
Full Article
Title: SAVe: Self-Supervised Audio-visual Deepfake Detection Exploiting Visual Artifacts and Audio-visual Misalignment
Abstract:
arXiv:2603.25140v1 Announce Type: cross Abstract: Multimodal deepfakes can exhibit subtle visual artifacts and cross-modal inconsistencies, which remain challenging to detect, especially when detectors are trained primarily on curated synthetic forgeries. Such synthetic dependence can introduce dataset and generator bias, limiting scalability and robustness to unseen manipulations. We propose SAVe, a self-supervised audio-visual deepfake detection framework that learns entirely on authentic vide
Abstract:
arXiv:2603.25140v1 Announce Type: cross Abstract: Multimodal deepfakes can exhibit subtle visual artifacts and cross-modal inconsistencies, which remain challenging to detect, especially when detectors are trained primarily on curated synthetic forgeries. Such synthetic dependence can introduce dataset and generator bias, limiting scalability and robustness to unseen manipulations. We propose SAVe, a self-supervised audio-visual deepfake detection framework that learns entirely on authentic vide
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