A challenge entry combining Wav2Vec-AASIST audio scores with lightweight handcrafted-feature video scores via calibration and maxout reports 92.78% AUC on AV-Deepfake1M++ testA.
Exposing Lip-syncing Deepfakes from Mouth Inconsistencies
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abstract
A lip-syncing deepfake is a digitally manipulated video in which a person's lip movements are created convincingly using AI models to match altered or entirely new audio. Lip-syncing deepfakes are a dangerous type of deepfakes as the artifacts are limited to the lip region and more difficult to discern. In this paper, we describe a novel approach, LIP-syncing detection based on mouth INConsistency (LIPINC), for lip-syncing deepfake detection by identifying temporal inconsistencies in the mouth region. These inconsistencies are seen in the adjacent frames and throughout the video. Our model can successfully capture these irregularities and outperforms the state-of-the-art methods on several benchmark deepfake datasets. Code is available at https://github.com/skrantidatta/LIPINC
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KLASSify to Verify: Audio-Visual Deepfake Detection Using SSL-based Audio and Handcrafted Visual Features
A challenge entry combining Wav2Vec-AASIST audio scores with lightweight handcrafted-feature video scores via calibration and maxout reports 92.78% AUC on AV-Deepfake1M++ testA.