  Recent developments in adversarial machine learning have highlighted the
importance of building robust AI systems to protect against increasingly
sophisticated attacks. While frameworks like AI Guardian are designed to defend
against these threats, they often rely on assumptions that can limit their
effectiveness. For example, they may assume attacks only come from one
direction or include adversarial images in their training data. Our proposal
suggests a different approach to the AI Guardian framework. Instead of
including adversarial examples in the training process, we propose training the
AI system without them. This aims to create a system that is inherently
resilient to a wider range of attacks. Our method focuses on a dynamic defense
strategy using stable diffusion that learns continuously and models threats
comprehensively. We believe this approach can lead to a more generalized and
robust defense against adversarial attacks.
  In this paper, we outline our proposed approach, including the theoretical
basis, experimental design, and expected impact on improving AI security
against adversarial threats.
