Audio-Visual Person-of-Interest DeepFake Detection

• URL: http://arxiv.org/abs/2204.03083v3
• Date: Thu, 18 May 2023 06:56:42 GMT
• Title: Audio-Visual Person-of-Interest DeepFake Detection
• Authors: Davide Cozzolino, Alessandro Pianese, Matthias Nie{\ss}ner, Luisa Verdoliva
• Abstract summary: The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. We leverage a contrastive learning paradigm to learn the moving-face and audio segment embeddings that are most discriminative for each identity. Our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos.
• Score: 77.04789677645682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face manipulation technology is advancing very rapidly, and new methods are being proposed day by day. The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. Our key insight is that each person has specific characteristics that a synthetic generator likely cannot reproduce. Accordingly, we extract audio-visual features which characterize the identity of a person, and use them to create a person-of-interest (POI) deepfake detector. We leverage a contrastive learning paradigm to learn the moving-face and audio segment embeddings that are most discriminative for each identity. As a result, when the video and/or audio of a person is manipulated, its representation in the embedding space becomes inconsistent with the real identity, allowing reliable detection. Training is carried out exclusively on real talking-face video; thus, the detector does not depend on any specific manipulation method and yields the highest generalization ability. In addition, our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos. Experiments on a wide variety of datasets confirm that our method ensures a SOTA performance, especially on low quality videos. Code is publicly available on-line at https://github.com/grip-unina/poi-forensics.

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