Hacking AI: Adversarial Attacks, Security Risks, and Defense Strategies

Hacking AI: Adversarial Attacks, Security Risks, and Defense Strategies by Dinesh Besiahgari and Krishna Kandi is your definitive guide to navigating the complex intersection of artificial intelligence and cybersecurity. As AI revolutionizes industries, it also opens new avenues for sophisticated attacks-ranging from adversarial inputs and data poisoning to model theft and deepfake manipulation. This comprehensive book equips cybersecurity professionals, AI developers, and tech enthusiasts with the critical knowledge needed to defend AI systems against evolving threats. Inside, you'll explore: The fundamentals and historical evolution of AI and cybersecurity How attackers exploit vulnerabilities in AI models, cloud services, and edge deployments Real-world case studies of AI security breaches Defense strategies to secure AI systems from training to deployment Practical testing methodologies, adversarial robustness techniques, and security frameworks Future risks, including generative AI threats, quantum computing impacts, and AI supply chain vulnerabilities With detailed insights, actionable strategies, and forward-looking approaches, Hacking AI empowers you to build resilient, trustworthy AI systems and protect sensitive data in an increasingly AI-driven world. Whether you're securing cloud-based AI, fortifying machine learning pipelines, or preparing for the next wave of cyber threats, this book offers the essential tools to future-proof your AI systems. Protect the future of AI-start here.