FairDeFace：评估人脸混淆方法的公平性与对抗鲁棒性
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
AI总结：
本文提出FairDeFace框架，通过统一基准、多种攻击与公平性指标，系统评估并比较七种人脸混淆方法的对抗鲁棒性及对不同人口群体的偏见。
摘要：
缺乏用于评估人脸混淆方法的通用平台和基准数据集一直是一个挑战，因为每种方法都使用任意的实验、数据集和指标进行测试。尽管先前的工作已经证明人脸识别系统对某些人口群体表现出偏见，但我们对人脸混淆方法公平性的理解仍存在巨大差距。提供公平的人脸混淆方法可以确保在不同人口群体之间实现公平保护，尤其是因为这些方法可用于保护弱势群体的隐私。为了解决这些差距，本文引入了一个名为FairDeFace的综合框架，旨在评估人脸混淆方法的对抗鲁棒性和公平性。该框架引入了一组模块，包括数据基准、人脸检测与识别算法、对抗模型、效用检测模型和公平性指标。FairDeFace作为一个通用平台，可以集成任何人脸混淆方法，从而允许进行严格的测试并与其他最先进的方法进行比较。在当前实现中，FairDeFace包含6种攻击，以及若干隐私、效用和公平性指标。利用FairDeFace，通过进行超过500次实验，我们评估并比较了七种人脸混淆方法的对抗鲁棒性。这一广泛的分析带来了许多有趣的发现，既涉及现有方法的鲁棒性程度，也涉及它们对某些性别或种族群体的偏见。FairDeFace还使用混淆和验证攻击的关注区域可视化，不仅展示在混淆过程中某些人口群体的哪些区域被主要改变，还通过混淆和验证的关注区域比较展示它们失败的原因。
英文摘要：
The lack of a common platform and benchmark datasets for evaluating face obfuscation methods has been a challenge, with every method being tested using arbitrary experiments, datasets, and metrics. While prior work has demonstrated that face recognition systems exhibit bias against some demographic groups, there exists a substantial gap in our understanding regarding the fairness of face obfuscation methods. Providing fair face obfuscation methods can ensure equitable protection across diverse demographic groups, especially since they can be used to preserve the privacy of vulnerable populations. To address these gaps, this paper introduces a comprehensive framework, named FairDeFace, designed to assess the adversarial robustness and fairness of face obfuscation methods. The framework introduces a set of modules encompassing data benchmarks, face detection and recognition algorithms, adversarial models, utility detection models, and fairness metrics. FairDeFace serves as a versatile platform where any face obfuscation method can be integrated, allowing for rigorous testing and comparison with other state-of-the-art methods. In its current implementation, FairDeFace incorporates 6 attacks, and several privacy, utility and fairness metrics. Using FairDeFace, and by conducting more than 500 experiments, we evaluated and compared the adversarial robustness of seven face obfuscation methods. This extensive analysis led to many interesting findings both in terms of the degree of robustness of existing methods and their biases against some gender or racial groups. FairDeFace also uses visualization of focused areas for both obfuscation and verification attacks to show not only which areas are mostly changed in the obfuscation process for some demographics, but also why they failed through focus area comparison of obfuscation and verification.
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。
