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.

我们的研究论文提出了一种基于动态防御策略和稳定扩散的方法，该方法旨在在不使用对抗性示例的情况下进行 AI 系统的训练，以创建一个更广泛范围攻击下具有内在韧性的系统，从而提供一种更广泛且更强大的针对对抗性攻击的防御。