StealthMask: Highly stealthy adversarial attack on face recognition system

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jian-Xun Mi, Mingxuan Chen, Tao Chen, Xiao Cheng
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引用次数: 0

Abstract

Convolutional Neural Networks (CNNs) based on deep learning algorithms are widely used in real-world scenarios. However, these networks are vulnerable to adversarial examples-maliciously crafted inputs that can cause the model to make incorrect predictions. The existence of adversarial examples presents a significant challenge to the field of deep learning, with profound implications for various aspects of our lives. In face recognition technology, adversarial examples pose a substantial security risk. In this paper, we propose a novel method for generating adversarial patches designed to be worn as masks. The perturbed mask is crafted to deceive face recognition models, thereby highlighting the security vulnerabilities inherent in this technology. Our experimental results demonstrate that the mask generated by the proposed method effectively misleads the face recognition system, achieving high attack success rates while maintaining necessary stealthiness and transferability. Moreover, our method successfully attacks commercial face recognition systems and real-world access control systems, exposing the vulnerabilities of existing face recognition technologies in security-critical applications. Notably, compared to traditional methods, our proposed method emphasizes the stealthiness of the adversarial mask more than traditional methods. To account for physical-world factors, such as distortion, rotation, and deformations, we integrate a specifically designed loss function, thereby enhancing the method’s stability and reliability in practical scenarios.

Abstract Image

StealthMask:对人脸识别系统的高度隐形对抗性攻击
基于深度学习算法的卷积神经网络(cnn)在现实场景中得到了广泛的应用。然而,这些网络很容易受到对抗性示例的攻击——恶意制作的输入可能导致模型做出错误的预测。对抗性例子的存在对深度学习领域提出了重大挑战,对我们生活的各个方面都有深远的影响。在人脸识别技术中,对抗性示例带来了巨大的安全风险。在本文中,我们提出了一种新的方法来生成设计为面具的对抗性补丁。受干扰的掩码被精心设计来欺骗人脸识别模型,从而突出了该技术固有的安全漏洞。实验结果表明,该方法生成的掩码有效地误导了人脸识别系统,在保持必要的隐身性和可转移性的同时,获得了较高的攻击成功率。此外,我们的方法成功地攻击了商用人脸识别系统和现实世界的访问控制系统,暴露了现有人脸识别技术在安全关键应用中的漏洞。值得注意的是,与传统方法相比,我们提出的方法比传统方法更强调对抗掩模的隐身性。为了考虑物理世界的因素,如扭曲、旋转和变形,我们集成了一个专门设计的损失函数,从而提高了该方法在实际场景中的稳定性和可靠性。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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