Delve into advanced adversarial attacks such as evasion and privacy breaches, and learn how to defend AI systems against model theft, data leakage, and generative AI threats. Understand privacy-preserving techniques and the security implications of GANs.

This course covers sophisticated attack vectors targeting deployed AI systems, including evasion attacks, model and data theft, and the misuse of generative adversarial networks (GANs) for deepfakes and cyberattacks. Learners will explore privacy-preserving AI methods, advanced anonymization, and the security challenges posed by generative technologies. By completing this course, participants will be able to identify, assess, and mitigate a wide range of privacy and generative AI threats. The course uses engaging video lectures, detailed readings, and knowledge checks to help learners recognize advanced adversarial techniques and apply effective defense strategies. Emphasis is placed on real-world scenarios and actionable security practices. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Adversarial AI Attacks, Mitigations, and Defense Strategies, by John Sotiropoulos. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.