EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection
The paper presents EMO-BOOST, a new framework aimed at improving deepfake detection by integrating emotion recognition. This multimodal approach combines traditional audio-visual detection methods with emotion-based cues to enhance generalization to unseen deepfake manipulations. The study demonstrates a 2.1% improvement in average cross-manipulation generalization AUC on the FakeAVCeleb dataset.
- ▪EMO-BOOST integrates high-level semantic cues from emotions into deepfake detection.
- ▪The framework combines an RGB- and acoustic-focused detector with an emotion-based detector called EmoForensics.
- ▪The study found that combining signals from both detectors enhances detection performance.
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Computer Science > Artificial Intelligence arXiv:2605.19630 (cs) [Submitted on 19 May 2026] Title:EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection Authors:Aritra Marik, Marcel Klemt, Anna Rohrbach View a PDF of the paper titled EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection, by Aritra Marik and 2 other authors View PDF HTML (experimental) Abstract:With every advancement in generative AI models, forensics is under increasing pressure. The constant emergence of new generation techniques makes it impossible to collect data for each manipulation to train a deepfake detection model.
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