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Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems

2019-05-09 02:58:45
Kazuya Kakizaki, Kosuke Yoshida

Abstract

Thanks to recent advances in Deep Neural Networks (DNNs), face recognition systems have achieved high accuracy in classification of a large number of face images. However, recent works demonstrate that DNNs could be vulnerable to adversarial examples and raise concerns about robustness of face recognition systems. In particular adversarial examples that are not restricted to small perturbations could be more serious risks since conventional certified defenses might be ineffective against them. To shed light on the vulnerability of the face recognition systems to this type of adversarial examples, we propose a flexible and efficient method to generate unrestricted adversarial examples using image translation techniques. Our method enables us to translate a source into any desired facial appearance with large perturbations so that target face recognition systems could be deceived. We demonstrate through our experiments that our method achieves about $90\%$ and $30\%$ attack success rates under a white- and black-box setting, respectively. We also illustrate that our generated images are perceptually realistic and maintain personal identity while the perturbations are large enough to defeat certified defenses.

Abstract (translated)

由于深神经网络(DNN)的最新进展,人脸识别系统在对大量人脸图像进行分类时已取得了较高的精度。然而,最近的研究表明,dnn可能容易受到对抗性例子的攻击,并引起对人脸识别系统鲁棒性的关注。尤其是不局限于小扰动的对抗性例子可能是更严重的风险,因为传统的认证防御可能对它们无效。为了揭示人脸识别系统对这类对抗性实例的脆弱性,我们提出了一种灵活有效的方法,利用图像翻译技术生成无限制的对抗性实例。我们的方法使我们能够将一个源转换成任何需要的具有大扰动的面部外观,从而使目标面部识别系统被欺骗。我们通过实验证明,在白盒和黑盒设置下,我们的方法分别达到了$90\%$和$30\%$的攻击成功率。我们还说明,我们生成的图像是感性现实的,并保持个人身份,而干扰足够大,以击败认证防御。

URL

https://arxiv.org/abs/1905.03421

PDF

https://arxiv.org/pdf/1905.03421.pdf


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