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This study addresses the limitations of existing face attack detection methods by integrating fine-grained textual descriptions of forgery cues into a visual recognition framework. Utilizing the large-scale MS-UFAD dataset, which comprises over 8 million attack images, the authors introduce the Dual Alignment Forgery Network (DAF-Net) to effectively align and leverage both visual and textual information. The results indicate that DAF-Net significantly enhances the generalizability and semantic richness of forgery representations, outperforming traditional vision-only and coarse-grained description methods.
Fine-grained textual cues can dramatically improve face attack detection, revealing vulnerabilities in existing systems that rely solely on visual data.
The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cues, leading most prior methods to treat face attack detection primarily as a visual recognition task. In this paper, building upon the large-scale MS-UFAD dataset which contains over 8 million attack images, we enrich each image with a fine-grained textual description of forgery cues. Furthermore, we propose a Dual Alignment Forgery Network(DAF-Net) to better leverage these textual information. Extensive experiments demonstrate that our approach extracts more generalizable and semantically meaningful forgery representations from attack images, outperforming both vision-only methods and approaches based on coarse-grained descriptions.
