This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to simulate these attacks on real-world use cases, such as self-driving cars. Our goal is to inform practitioners and researchers about the challenges and opportunities in defending AI systems against adversarial threats. By providing a comprehensive comparison of different attack methods, we aim to contribute to the development of more robust and secure AI systems.

翻译：本研究全面概述了针对人工智能和机器学习模型的对抗攻击，探讨了各类攻击类型、技术及其潜在危害。我们深入分析了其商业影响、缓解策略以及未来研究方向。为获得实践洞察，我们采用Adversarial Robustness Toolbox (ART) [1]库在自动驾驶汽车等现实应用场景中模拟这些攻击。我们的目标是让从业者和研究者了解在防御对抗性威胁时面临的挑战与机遇。通过对不同攻击方法进行全面比较，我们旨在为开发更鲁棒、更安全的人工智能系统作出贡献。