Papers › Multimodal Forgery Detection Using Ensemble Learning

Multimodal Forgery Detection Using Ensemble Learning

7 Nov 2022Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2022 11archive 2025-07-28

Ammarah Hashmi, Sahibzada Adil Shahzad, Wasim Ahmad, Chia Wen Lin, Yu Tsao, Hsin-Min Wang

The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized 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.

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