论文标题
关于面部识别算法的鲁棒性，以防止攻击和偏见
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
论文作者
论文摘要
面部识别算法表现出很高的识别性能，这表明对现实世界应用的适用性。尽管精度提高了，但这些算法对攻击和偏见的鲁棒性受到挑战。本文总结了面部识别算法的鲁棒性受到挑战的不同方式，这可能会严重影响其预期的工作。已经讨论了不同类型的攻击，例如物理表现攻击，伪装/化妆，数字对抗性攻击以及使用gan的变形/篡改。我们还提出了关于偏见对面部识别模型的影响的讨论，并展示了年龄和性别变化等因素会影响现代算法的性能。本文还提出了这些挑战的潜在原因以及提高面部识别模型鲁棒性的未来研究方向。
Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.
