Towards Face Encryption by Generating Adversarial Identity Masks
Towards Face Encryption by Generating Adversarial Identity Masks
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DOI：
10.1109/iccv48922.2021.00387
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Xiao Yang;Yinpeng Dong;Tianyu Pang;Hang Su;Jun Zhu;Yuefeng Chen;H. Xue
中科院分区：
文献类型：
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作者：
Xiao Yang;Yinpeng Dong;Tianyu Pang;Hang Su;Jun Zhu;Yuefeng Chen;H. Xue
随着数以亿计的个人数据通过社交媒体和网络共享，数据的隐私和安全越来越引起人们的关注。已经进行了几次尝试，借助于例如图像模糊技术来减轻面部照片中身份信息的泄漏。然而，目前的大多数结果要么在感知上不令人满意，要么对人脸识别系统无效。我们在本文中的目标是开发一种可以对个人照片进行加密的技术，以便它们可以保护用户免受未经授权的人脸识别系统的攻击，但在视觉上与人类的原始版本相同。为了达到这一目的，我们提出了一种有针对性的身份保护迭代方法(TIP-IM)来生成可以覆盖在人脸图像上的敌意身份掩模，从而在不牺牲视觉质量的情况下隐藏原始身份。大量的实验表明，在实际测试场景下，TIP-IM对各种最先进的人脸识别模型提供了95%以上的保护成功率。此外，我们还展示了我们的方法在一个商业API服务上的实用和有效的适用性。
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually identical to the original version for human beings. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality. Extensive experiments demonstrate that TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models under practical test scenarios. Besides, we also show the practical and effective applicability of our method on a commercial API service.