Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model

• URL: http://arxiv.org/abs/2404.05583v2
• Date: Wed, 5 Jun 2024 06:29:37 GMT
• Title: Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model
• Authors: Yue-Hua Han, Tai-Ming Huang, Shu-Tzu Lo, Po-Han Huang, Kai-Lung Hua, Jun-Cheng Chen,
• Abstract summary: We propose a novel Deepfake detection approach by adapting the Foundation Models with rich information encoded inside. Inspired by the recent advances of parameter efficient fine-tuning, we propose a novel side-network-based decoder. Our approach exhibits superior effectiveness in identifying unseen Deepfake samples, achieving notable performance improvement.
• Score: 15.61920157541529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of deep learning, generative models have enabled the creation of highly realistic synthetic images, presenting challenges due to their potential misuse. While research in Deepfake detection has grown rapidly in response, many detection methods struggle with unseen Deepfakes generated by new synthesis techniques. To address this generalisation challenge, we propose a novel Deepfake detection approach by adapting the Foundation Models with rich information encoded inside, specifically using the image encoder from CLIP which has demonstrated strong zero-shot capability for downstream tasks. Inspired by the recent advances of parameter efficient fine-tuning, we propose a novel side-network-based decoder to extract spatial and temporal cues from the given video clip, with the promotion of the Facial Component Guidance (FCG) to encourage the spatial feature to include features of key facial parts for more robust and general Deepfake detection. Through extensive cross-dataset evaluations, our approach exhibits superior effectiveness in identifying unseen Deepfake samples, achieving notable performance improvement even with limited training samples and manipulation types. Our model secures an average performance enhancement of 0.9\% AUROC in cross-dataset assessments comparing with state-of-the-art methods, especially a significant lead of achieving 4.4\% improvement on the challenging DFDC dataset.

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