Face De-Spoofing: Anti-Spoofing via Noise Modeling
This addresses security vulnerabilities in biometric systems by enhancing spoof detection, though it is incremental as it builds on noise modeling and denoising concepts.
The paper tackles face anti-spoofing by proposing a face de-spoofing method that decomposes spoof faces into spoof noise and live faces, using the noise for classification, and reports promising improvements on multiple databases.
Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an indivisible unit, and process it holistically, without explicit modeling of the spoofing process. In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification. A CNN architecture with proper constraints and supervisions is proposed to overcome the problem of having no ground truth for the decomposition. We evaluate the proposed method on multiple face anti-spoofing databases. The results show promising improvements due to our spoof noise modeling. Moreover, the estimated spoof noise provides a visualization which helps to understand the added spoof noise by each spoof medium.