DivTrackee 对比 DynTracker：针对动态人脸识别策略提升反人脸识别的多样性
DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy
AI总结：
针对动态人脸识别追踪策略，提出通过文本引导生成与多样性对抗损失增强反人脸识别图像多样性，以有效抵御动态追踪。
摘要：
人脸识别（FR）模型的广泛普及引发了对其潜在滥用的严重担忧，从而推动了反人脸识别（AFR）技术的发展以保护用户面部隐私。在本文中，我们认为先前文献中评估 AFR 有效性时主要采用的静态 FR 策略，无法真实刻画那些旨在追踪特定目标身份的坚定追踪者的实际能力。具体而言，我们引入了 DynTracker，一种动态 FR 策略，其中模型的图库数据库会利用新识别出的目标身份图像进行迭代更新。令人惊讶的是，这种简单的方法使得所有现有的 AFR 保护措施失效。为了缓解 DynTracker 带来的隐私威胁，我们主张在 AFR 保护的图像中明确促进多样性。我们假设多样性的缺乏是现有 AFR 方法失败的主要原因。具体来说，我们开发了 DivTrackee，一种构建多样化 AFR 保护的新方法，它基于文本引导的图像生成框架和促进多样性的对抗损失。通过在多种图像基准和特征提取器上的全面实验，我们证明了 DynTracker 在破解现有 AFR 方法方面的强大能力，以及 DivTrackee 在防止用户面部图像被动态 FR 策略识别方面的优越性。我们相信，我们的工作可以作为开发更有效的 AFR 方法以保护用户面部隐私免受坚定追踪者侵害的重要第一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
