Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues

• URL: http://arxiv.org/abs/2212.14629v2
• Date: Tue, 19 Sep 2023 09:41:02 GMT
• Title: Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues
• Authors: Decheng Liu, Zeyang Zheng, Chunlei Peng, Yukai Wang, Nannan Wang, Xinbo Gao
• Abstract summary: We propose a novel Hierarchical Forgery for Multi-modality Face Forgery Detection (HFC-MFFD) The HFC-MFFD learns robust patches-based hybrid representation to enhance forgery authentication in multiple-modality scenarios. The specific hierarchical face forgery is proposed to alleviate the class imbalance problem and further boost detection performance.
• Score: 61.37306431455152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face forgery detection plays an important role in personal privacy and social security. With the development of adversarial generative models, high-quality forgery images become more and more indistinguishable from real to humans. Existing methods always regard as forgery detection task as the common binary or multi-label classification, and ignore exploring diverse multi-modality forgery image types, e.g. visible light spectrum and near-infrared scenarios. In this paper, we propose a novel Hierarchical Forgery Classifier for Multi-modality Face Forgery Detection (HFC-MFFD), which could effectively learn robust patches-based hybrid domain representation to enhance forgery authentication in multiple-modality scenarios. The local spatial hybrid domain feature module is designed to explore strong discriminative forgery clues both in the image and frequency domain in local distinct face regions. Furthermore, the specific hierarchical face forgery classifier is proposed to alleviate the class imbalance problem and further boost detection performance. Experimental results on representative multi-modality face forgery datasets demonstrate the superior performance of the proposed HFC-MFFD compared with state-of-the-art algorithms. The source code and models are publicly available at https://github.com/EdWhites/HFC-MFFD.

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