Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues
Decheng Liu, Zeyang Zheng, Chunlei Peng, Yukai Wang, Nannan Wang,, Xinbo Gao

TL;DR
This paper introduces a Hierarchical Forgery Classifier for Multi-modality Face Forgery Detection that leverages hybrid domain features and hierarchical classification to improve detection accuracy across different imaging modalities.
Contribution
The paper proposes a novel multi-modality face forgery detection framework with a hybrid domain feature module and a hierarchical classifier, addressing class imbalance and enhancing robustness.
Findings
Outperforms state-of-the-art algorithms on multi-modality datasets.
Effectively detects face forgeries in visible light and near-infrared scenarios.
Utilizes local spatial hybrid domain features for improved discrimination.
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…
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Taxonomy
TopicsDigital Media Forensic Detection · Face recognition and analysis · Generative Adversarial Networks and Image Synthesis
