Neural networks demonstrate inherent vulnerability to small, non-random perturbations, emerging as adversarial attacks. Such attacks, born from the gradient of the loss function relative to the input, are discerned as input conjugates, revealing a systemic fragility within the network structure. Intriguingly, a mathematical congruence manifests between this mechanism and the quantum physics' uncertainty principle, casting light on a hitherto unanticipated interdisciplinarity. This inherent susceptibility within neural network systems is generally intrinsic, highlighting not only the innate vulnerability of these networks but also suggesting potential advancements in the interdisciplinary area for understanding these black-box networks.

翻译：神经网络对微小、非随机扰动表现出固有的脆弱性，这种扰动即为对抗性攻击。此类攻击源于损失函数相对于输入的梯度，可视为输入共轭量，揭示了网络结构内部的系统性脆弱性。值得注意的是，该机制与量子物理学中的不确定性原理之间存在数学上的同构性，揭示了此前未曾预料到的跨学科关联。神经网络系统中这种固有的脆弱性本质上是内在的，不仅凸显了这些网络的先天缺陷，还暗示了在理解这类黑箱网络的跨学科领域中潜在的发展方向。