神经网络展示了小的脆弱性，非随机扰动，正在成为对抗性攻击。这样的攻击,从损失函数相对于输入的梯度中诞生，被识别为输入共轭，揭示了网络结构内的系统脆弱性。有趣的是，数学上的一致性体现在这种机制和量子物理学的不确定性原理之间，投射出迄今为止意想不到的跨学科性。神经网络系统中的这种固有敏感性通常是内在的，不仅强调了这些网络的先天脆弱性，但也提出了在跨学科领域的潜在进步，以了解这些黑匣子网络。
Neural networks demonstrate 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.
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