Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues
This addresses the problem of detecting manipulated multimedia content for security and media integrity, with incremental novelty in combining modalities and emotions.
The paper tackles deepfake detection by analyzing audio-visual similarity and affective cues, achieving an AUC of 84.4% on DFDC and 96.6% on DF-TIMIT datasets.
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively. To the best of our knowledge, ours is the first approach that simultaneously exploits audio and video modalities and also perceived emotions from the two modalities for deepfake detection.