APF: An Adversarial Privacy-preserving Filter to Protect Portrait Information

Xian Zhao, Jiaming Zhang, Xiaowen Huang

Abstract: While widely adopted in practical applications, face recognition has been disputed on the malicious use of face images and potential privacy issues. Online photo sharing services accidentally act as the main approach for the malicious crawlers to exploit face recognition to access portrait privacy. In this demo, we propose an adversarial privacy-preserving filter, which can preserve face image from malicious face recognition algorithms. This filter is generated by an end-cloud collaborated adversarial attack framework consisting of three modules: (1) Image-specific gradient generation module, to extract image-specific gradient in the user end; (2) Adversarial gradient transfer module, to fine-tune the image-specific gradient in the server; and (3) Universal adversarial perturbation enhancement module, to append image-independent perturbation to derive the final adversarial perturbation. A short video about our system is available at https://github.com/Anonymity-for-submission/3247.