Emo-Boost: Emotion Cues Improve Deepfake Detection Generalization
A new framework called Emo-Boost has been introduced by researchers to enhance the detection of deepfakes by utilizing emotions as a significant semantic indicator, thereby improving its adaptability to previously unseen manipulation techniques. This system integrates a standard RGB and acoustic deepfake detector with EmoForensics, which focuses on emotion detection through both visual and auditory recognition modules. EmoForensics effectively models the temporal consistency of emotional representations across audio and video formats. This method tackles the prevalent challenge of identifying deepfakes created by unfamiliar techniques, a critical concern in current forensic studies. The findings are available on arXiv (2605.19630).
Key facts
- Emo-Boost is a multimodal deepfake detection framework.
- It uses emotion as a high-level semantic cue.
- It fuses an off-the-shelf RGB and acoustic detector with EmoForensics.
- EmoForensics uses vision and audio emotion recognition modules.
- It models intra- and inter-modal temporal consistency in emotion representations.
- The goal is to generalize to unseen deepfake manipulation types.
- The research addresses a major challenge in deepfake detection.
- The paper is available on arXiv with ID 2605.19630.
Entities
Institutions
- arXiv