Adversarial Infrared

Adversarial infrared research focuses on developing and mitigating attacks that deceive infrared-based object detection systems, primarily used in autonomous driving and security applications. Current research explores various methods to generate adversarial perturbations, including strategically placed patches, altered geometries, and specially designed clothing, often employing optimization algorithms like genetic algorithms and particle swarm optimization to maximize attack effectiveness across multiple viewpoints. These studies highlight vulnerabilities in deep learning models used for infrared detection and underscore the need for robust and reliable systems to ensure safety and security in real-world applications. The findings are crucial for improving the resilience of infrared-based technologies against malicious manipulation.