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.

翻译：机器学习中的对抗样本因其能以看似无害的输入扰动欺骗模型、并可能引发严重后果而成为研究焦点。本研究全面探索了对抗性机器学习模型，揭示了其内在复杂性与可解释性。通过引入纠缠的概念，我们的研究发现机器学习模型复杂度与爱因斯坦狭义相对论之间存在有趣的关联。具体而言，我们定义了计算纠缠，并证明远距离特征样本可展现出类似于量子领域纠缠的强相关性。这一发现挑战了描述当代机器学习模型中对抗迁移性现象的传统视角。通过将计算过程中的时间膨胀与长度收缩相对论效应进行类比，我们得以更深入地理解对抗性机器学习，为这一快速发展的领域构建更鲁棒、更具可解释性的模型铺平了道路。