SAVe: Self-Supervised Audio-visual Deepfake Detection Exploiting Visual Artifacts and Audio-visual Misalignment

1. 1Learn from authentic videos without relying on curated synthetic forgeries
2. 2Exploit visual artifacts and audio-visual misalignment for deepfake detection
3. 3Train a self-supervised model to detect inconsistencies between audio and visual modalities
4. 4Evaluate the model on unseen manipulations to test its scalability and robustness

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

💡 Self-supervised learning can be effective for deepfake detection, reducing dependence on curated synthetic forgeries and improving scalability and robustness

💡 Detect deepfakes with SAVe, a self-supervised audio-visual framework that exploits visual artifacts and audio-visual misalignment