Cancelable Face Recognition Using Deep Steganography

In biometrics, the secure transfer and storage of biometric samples are important for protecting the privacy and security of the data subject. One of the methods for authentication while protecting biometric samples is cancelable biometrics, which performs transformation of features and uses the transformed features for authentication. Among the methods of cancelable biometrics, steganography-based approaches have been proposed, in which secret information is embedded in another to hide its existence. In this paper, we propose cancelable biometrics based on deep steganography for face recognition. We embed a face image or its face features into a cover image to generate a stego image with the same appearance as the cover image. By using a dedicated face feature extractor, we can perform face recognition without restoring the embedded face image or face features from the stego image. We demonstrate the effectiveness of the proposed method compared to conventional steganography-based methods through performance and security evaluation experiments using public face image datasets. In addition, we present one of the potential applications of the proposed method to improve the security of face recognition by using a QR code with a one-time password for the cover image.