DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization

Deepfake technology has rapidly advanced, posing significant threats to information integrity and societal trust. While significant progress has been made in detecting deepfakes, the simultaneous manipulation of audio and visual modalities, sometimes at small parts but still altering the meaning, presents a more challenging detection scenario. We present a novel audio-visual deepfake detection framework that leverages the inter-modality differences in machine perception of speech, based on the assumption that in real samples - in contrast to deepfakes - visual and audio signals coincide in terms of information. Our framework leverages features from deep networks that specialize in video and audio speech recognition to spot frame-level cross-modal incongruities, and in that way to temporally localize the deepfake forgery. To this end, DiMoDif employs a Transformer encoder-based architecture with a feature pyramid scheme and local attention, and optimizes the detection model through a composite loss function accounting for frame-level detections and fake intervals localization. DiMoDif outperforms the state-of-the-art on the Temporal Forgery Localization task by +47.88% AP@0.75 on AV-Deepfake1M, and performs on-par on LAV-DF. On the Deepfake Detection task, it outperforms the state-of-the-art by +30.5% AUC on AV-Deepfake1M, +2.8% AUC on FakeAVCeleb, and performs on-par on LAV-DF. Code available at https://github.com/mever-team/dimodif.