Having established the general principles of adversarial machine learning, we now turn to their application within specific fields. This chapter examines how adversarial attacks and defenses operate differently depending on the domain, focusing on Computer Vision (CV), Natural Language Processing (NLP), and Reinforcement Learning (RL).

We will analyze techniques adapted for image data, methods targeting text-based models, and strategies aimed at manipulating learned agent policies. You will gain insight into domain-specific constraints, such as maintaining perceptual similarity in images or semantic coherence in text when crafting attacks. Furthermore, the challenge of crafting attacks effective in physical environments will be addressed. This targeted analysis is essential for understanding and mitigating vulnerabilities specific to different types of AI applications.