Exposing Lip-syncing Deepfakes from Mouth Inconsistencies
This addresses the challenge of discerning dangerous lip-syncing deepfakes, which are difficult to detect due to limited artifacts, for applications in media forensics and security.
The paper tackles the problem of detecting lip-syncing deepfakes by identifying temporal inconsistencies in the mouth region, and the proposed LIPINC model outperforms state-of-the-art methods on several benchmark datasets.
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