Delving into Sequential Patches for Deepfake Detection

• URL: http://arxiv.org/abs/2207.02803v1
• Date: Wed, 6 Jul 2022 16:46:30 GMT
• Title: Delving into Sequential Patches for Deepfake Detection
• Authors: Jiazhi Guan, Hang Zhou, Zhibin Hong, Errui Ding, Jingdong Wang, Chengbin Quan, Youjian Zhao
• Abstract summary: Recent advances in face forgery techniques produce nearly untraceable deepfake videos, which could be leveraged with malicious intentions. Previous studies has identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake methods. We propose the Local- & Temporal-aware Transformer-based Deepfake Detection framework, which adopts a local-to-global learning protocol.
• Score: 64.19468088546743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in face forgery techniques produce nearly visually untraceable deepfake videos, which could be leveraged with malicious intentions. As a result, researchers have been devoted to deepfake detection. Previous studies has identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake methods, however, they still suffer from robustness problem against post-processings. In this work, we propose the Local- & Temporal-aware Transformer-based Deepfake Detection (LTTD) framework, which adopts a local-to-global learning protocol with a particular focus on the valuable temporal information within local sequences. Specifically, we propose a Local Sequence Transformer (LST), which models the temporal consistency on sequences of restricted spatial regions, where low-level information is hierarchically enhanced with shallow layers of learned 3D filters. Based on the local temporal embeddings, we then achieve the final classification in a global contrastive way. Extensive experiments on popular datasets validate that our approach effectively spots local forgery cues and achieves state-of-the-art performance.

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