TL;DR
This paper introduces an end-cloud collaborative adversarial attack method called APF to protect online face images from malicious recognition, balancing privacy, utility, and accessibility.
Contribution
The work presents a novel end-cloud adversarial attack framework that generates privacy-preserving perturbations for face images shared online, with extensive validation and a prototype application.
Findings
Effective in preventing face recognition on multiple datasets
Balances privacy protection with image utility
Efficient and practical for real-world deployment
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
