Cost-effective and robust adversarial patch attacks in real-world scenarios

Deep neural networks (DNNs) are crucial in self-driving car technology but are vulnerable to adversarial attacks that manipulate visual input to mislead decision-making processes. Traditional physical adversarial attacks, such as patch-based methods, often use noticeable patches on target objects, making them easily detectable by humans. Recent advancements include camera-based attacks that apply perturbations directly to camera lenses for stealthier impacts, although these typically require multiple patches and insider model knowledge, making them impractical. We introduce a camera patch attack using a single patch optimized via particle swarm optimization (PSO) to enhance adversarial effects without extensive modifications. The method is tested digitally and physically, proving its robustness and efficacy in generating deceptive inputs that challenge both object detection and image classification systems while also exploring defensive measures against such attacks. Ultimately, an ensemble attack strategy is proposed to enhance transferability. Robustness is demonstrated through successful attacks on advanced DNNs, achieving success rates >80% in all scenarios. We support the attention and consideration of the approach framework due to its simplicity, robustness, and stealthiness, as it offers a means of achieving efficient, stealthy, and effective attacks.