Adversarial examples in machine learning has emerged as a focal point of research due to their remarkable ability to deceive models with seemingly inconspicuous input perturbations, potentially resulting in severe consequences. In this study, we embark on a comprehensive exploration of adversarial machine learning models, shedding light on their intrinsic complexity and interpretability. Our investigation reveals intriguing links between machine learning model complexity and Einstein's theory of special relativity, through the concept of entanglement. More specific, we define entanglement computationally and demonstrate that distant feature samples can exhibit strong correlations, akin to entanglement in quantum realm. This revelation challenges conventional perspectives in describing the phenomenon of adversarial transferability observed in contemporary machine learning models. By drawing parallels with the relativistic effects of time dilation and length contraction during computation, we gain deeper insights into adversarial machine learning, paving the way for more robust and interpretable models in this rapidly evolving field.

通过对对抗性机器学习模型的综合探索，我们发现了它们内在的复杂性和可解释性，并揭示了它们与爱因斯坦的特殊相对论之间的有趣联系，涉及到纠缠的概念。这一发现对描述当代机器学习模型中观察到的对抗可迁移性现象的传统观点提出了挑战。通过将计算过程中的时间扭曲和长度收缩与相对性效应进行类比，我们能够更深入地了解对抗性机器学习，从而为这个快速发展的领域中更健壮且可解释的模型铺平道路。

关于对抗机器学习中计算纠缠及其解释