computer-vision-5
Multimodal Forgery Detection
3260 papers • 126 benchmarks • 313 datasets
Multimodal Forgery Detection task is a deep forgery detection method which uses both video and audio.
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FakeAVCeleb
FakeAVCeleb
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Most implemented papers
Multimodal Forgery Detection Using Ensemble Learning
Ammarah Hashmi, Sahibzada Adil Shahzad, Wasim Ahmad, Chia-Wen Lin, Yu Tsao, Hsin-Min Wang•Sun Nov 06 2022
The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AI-synthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models.