Generating and Restoring Private Face Images for Internet of Vehicles Based on Semantic Features and Adversarial Examples
Jingjing Yang, Jiaxing Liu, Runkai Han, Jinzhao Wu · IEEE Transactions on Intelligent Transportation Systems · 2021
The disclosure of face image features can seriously threaten the security of user information, which limits the application of face recognition technology in Internet of Vehicles. This paper proposes a new method of generating and restoring private face images based on semantic features and adversarial examples. The Segnet network first segments the face images semantically, then a generative adversarial network generates adversarial examples and perturbs the semantic features of the face image. The perturbation position can be accurately controlled through a coefficient matrix as the identity tag of the face image is concealed steganographically. A restoration network is trained to extract the real identity tag from the private face image using a discriminator against the generation network, then it restores the private face image to its original state. Compared to other state-of-the-art methods, private face images generated by the proposed method experimentally show high detection resistance, better quality, and stronger median filtering defense.