Infrared Attack

Infrared attacks exploit vulnerabilities in infrared-based AI systems, primarily object detectors, by generating adversarial perturbations that cause misclassification. Current research focuses on developing effective and stealthy physical attacks, using methods like strategically placed thermal patches or geometric shapes to manipulate infrared signatures, often employing optimization algorithms such as Particle Swarm Optimization to enhance attack success rates and transferability across different models. These attacks highlight significant security risks in various applications, including autonomous driving and security systems, prompting investigation into robust defense mechanisms and improved system design. The ease of implementation for some of these attacks underscores the urgent need for further research and development of countermeasures.