DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy

TL;DR

This paper introduces DynTracker, a dynamic facial recognition strategy that updates target images iteratively, exposing weaknesses in existing anti-Facial Recognition protections, and proposes DivTrackee, a diversity-promoting method to enhance privacy defenses.

Contribution

We propose DynTracker, a novel dynamic FR strategy that challenges existing AFR methods, and introduce DivTrackee, a diversity-based approach to improve facial privacy protection.

Findings

DynTracker effectively breaks existing AFR protections.

DivTrackee significantly improves privacy against dynamic FR strategies.

Experimental results validate the superiority of DivTrackee over prior methods.

Abstract

The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-facial recognition (AFR) to protect user facial privacy. In this paper, we argue that the static FR strategy, predominantly adopted in prior literature for evaluating AFR efficacy, cannot faithfully characterize the actual capabilities of determined trackers who aim to track a specific target identity. In particular, we introduce DynTracker, a dynamic FR strategy where the model's gallery database is iteratively updated with newly recognized target identity images. Surprisingly, such a simple approach renders all the existing AFR protections ineffective. To mitigate the privacy threats posed by DynTracker, we advocate for explicitly promoting diversity in the AFR-protected images. We hypothesize that the lack of diversity is the primary cause of the failure of existing AFR methods. Specifically, we develop DivTrackee, a novel method for crafting diverse AFR protections that builds upon a text-guided image generation framework and diversity-promoting adversarial losses. Through comprehensive experiments on various image benchmarks and feature extractors, we demonstrate DynTracker's strength in breaking existing AFR methods and the superiority of DivTrackee in preventing user facial images from being identified by dynamic FR strategies. We believe our work can act as an important initial step towards developing more effective AFR methods for protecting user facial privacy against determined trackers.