Uncovering Transferability in Adversarial Machine Learning

The chapter explores the intriguing concept of transferability in adversarial attacks on machine learning models, specifically focusing on the manipulation of training data to induce mayhem and potential dangers. It discusses the implications of transferring adversarial examples across different models and the shared data leading to similar outcomes, shedding light on a new perspective in the adversarial machine learning community. The conversation delves into the historical context and experiments showcasing how adversarial examples can transcend model differences, unveiling a fascinating aspect of exploiting vulnerabilities in machine learning systems.