Modeling Electromagnetic Signal Injection Attacks on Camera-based Smart Systems: Applications and Mitigation

• URL: http://arxiv.org/abs/2408.05124v1
• Date: Fri, 9 Aug 2024 15:33:28 GMT
• Title: Modeling Electromagnetic Signal Injection Attacks on Camera-based Smart Systems: Applications and Mitigation
• Authors: Youqian Zhang, Michael Cheung, Chunxi Yang, Xinwei Zhai, Zitong Shen, Xinyu Ji, Eugene Y. Fu, Sze-Yiu Chau, Xiapu Luo,
• Abstract summary: electromagnetic waves pose a threat to safety- or security-critical systems. Such attacks enable attackers to manipulate the images remotely, leading to incorrect AI decisions. We present a pilot study on adversarial training to improve their robustness against attacks.
• Score: 18.909937495767313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous safety- or security-critical systems depend on cameras to perceive their surroundings, further allowing artificial intelligence (AI) to analyze the captured images to make important decisions. However, a concerning attack vector has emerged, namely, electromagnetic waves, which pose a threat to the integrity of these systems. Such attacks enable attackers to manipulate the images remotely, leading to incorrect AI decisions, e.g., autonomous vehicles missing detecting obstacles ahead resulting in collisions. The lack of understanding regarding how different systems react to such attacks poses a significant security risk. Furthermore, no effective solutions have been demonstrated to mitigate this threat. To address these gaps, we modeled the attacks and developed a simulation method for generating adversarial images. Through rigorous analysis, we confirmed that the effects of the simulated adversarial images are indistinguishable from those from real attacks. This method enables researchers and engineers to rapidly assess the susceptibility of various AI vision applications to these attacks, without the need for constructing complicated attack devices. In our experiments, most of the models demonstrated vulnerabilities to these attacks, emphasizing the need to enhance their robustness. Fortunately, our modeling and simulation method serves as a stepping stone toward developing more resilient models. We present a pilot study on adversarial training to improve their robustness against attacks, and our results demonstrate a significant improvement by recovering up to 91% performance, offering a promising direction for mitigating this threat.

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