  While widely adopted in practical applications, face recognition has been
critically discussed regarding the malicious use of face images and the
potential privacy problems, e.g., deceiving payment system and causing personal
sabotage. Online photo sharing services unintentionally act as the main
repository for malicious crawler and face recognition applications. This work
aims to develop a privacy-preserving solution, called Adversarial
Privacy-preserving Filter (APF), to protect the online shared face images from
being maliciously used.We propose an end-cloud collaborated adversarial attack
solution to satisfy requirements of privacy, utility and nonaccessibility.
Specifically, the solutions consist of three modules: (1) image-specific
gradient generation, to extract image-specific gradient in the user end with a
compressed probe model; (2) adversarial gradient transfer, to fine-tune the
image-specific gradient in the server cloud; and (3) universal adversarial
perturbation enhancement, to append image-independent perturbation to derive
the final adversarial noise. Extensive experiments on three datasets validate
the effectiveness and efficiency of the proposed solution. A prototype
application is also released for further evaluation.We hope the end-cloud
collaborated attack framework could shed light on addressing the issue of
online multimedia sharing privacy-preserving issues from user side.
