University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Gaoxin District, Chengdu, 611731, Sichuan, China; Chinese Academy of Military Science, No. 53 East Street, Fengtai District, Beijing, 100071, China.
University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Gaoxin District, Chengdu, 611731, Sichuan, China.
Neural Netw. 2024 Jul;175:106310. doi: 10.1016/j.neunet.2024.106310. Epub 2024 Apr 9.
Thermal infrared detectors have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. Recent works use bulb plate, "QR" suit, and infrared patches as physical perturbations to perform white-box attacks on thermal infrared detectors, which are effective but not practical for real-world scenarios. Some researchers have tried to utilize hot and cold blocks as physical perturbations for black-box attacks on thermal infrared detectors. However, this attempts has not yielded robust and multi-view physical attacks, indicating limitations in the approach. To overcome the limitations of existing approaches, we introduce a novel black-box physical attack method, called adversarial infrared blocks (AdvIB). By optimizing the physical parameters of the infrared blocks and deploying them to pedestrians from multiple views, including the front, side, and back, AdvIB can execute robust and multi-view attacks on thermal infrared detectors. Our physical tests show that the proposed method achieves a success rate of over 80% under most distance and view conditions, validating its effectiveness. For stealthiness, our method involves attaching the adversarial infrared block to the inside of clothing, enhancing its stealthiness. Additionally, we perform comprehensive experiments and compare the experimental results with baseline to verify the robustness of our method. In summary, AdvIB allows for potent multi-view black-box attacks, profoundly influencing ethical considerations in today's society. Potential consequences, including disasters from technology misuse and attackers' legal liability, highlight crucial ethical and security issues associated with AdvIB. Considering these concerns, we urge heightened attention to the proposed AdvIB. Our code can be accessed from the following link: https://github.com/ChengYinHu/AdvIB.git.
热红外探测器在行人检测和自动驾驶等领域有广泛的应用,其安全性能备受关注。最近的研究工作使用灯泡板、“QR”套装和红外补丁等物理扰动,对热红外探测器进行白盒攻击,这些攻击虽然有效,但在实际场景中并不实用。一些研究人员试图利用热块和冷块对热红外探测器进行黑盒攻击。然而,这种尝试并没有产生稳健的多视角物理攻击,表明该方法存在局限性。为了克服现有方法的局限性,我们提出了一种新的黑盒物理攻击方法,称为对抗性红外块(AdvIB)。通过优化红外块的物理参数,并从多个视角(包括正面、侧面和背面)向行人部署红外块,AdvIB 可以对热红外探测器执行稳健的多视角攻击。我们的物理测试表明,在大多数距离和视角条件下,该方法的成功率超过 80%,验证了其有效性。为了提高隐蔽性,我们的方法将对抗性红外块附着在衣服内部,增强其隐蔽性。此外,我们进行了全面的实验,并将实验结果与基线进行比较,以验证我们方法的鲁棒性。总之,AdvIB 允许进行强大的多视角黑盒攻击,对当今社会的伦理考量产生深远影响。潜在的后果,包括技术滥用引发的灾难和攻击者的法律责任,凸显了与 AdvIB 相关的关键伦理和安全问题。考虑到这些问题,我们强烈呼吁关注 AdvIB。我们的代码可以从以下链接访问:https://github.com/ChengYinHu/AdvIB.git。