标题： 基于熵的卷积神经网络非侵入式可靠性监控 显示英文标题

标题： Entropy-Based Non-Invasive Reliability Monitoring of Convolutional Neural Networks

摘要： 卷积神经网络（CNNs）已成为现代计算机视觉的基础，在各种图像识别任务中实现了前所未有的准确性。 尽管这些网络在分布内数据上表现出色，但它们对难以察觉的对抗性扰动仍然脆弱，这些扰动会导致高置信度的错误分类。 然而，现有的检测方法要么需要昂贵的重新训练，要么需要修改网络架构，或者会降低在干净输入上的性能。 在这里，我们表明对抗性扰动会在CNN激活中产生立即且可检测的熵签名，可以在不修改模型的情况下进行监测。 使用VGG-16上的并行熵监测，我们证明对抗性输入在早期卷积层中始终使激活熵变化7%，从而以低于20%的误报率和漏报率实现90%的检测准确率。 干净和对抗性熵分布之间的完全分离表明，CNN在其激活模式中固有地编码了分布变化。 这项工作确立了仅通过激活熵就可以评估CNN的可靠性，从而实现了自我诊断视觉系统的实际部署，能够在不损害原始模型性能的情况下实时检测对抗性输入。 显示英文摘要

摘要： Convolutional Neural Networks (CNNs) have become the foundation of modern computer vision, achieving unprecedented accuracy across diverse image recognition tasks. While these networks excel on in-distribution data, they remain vulnerable to adversarial perturbations imperceptible input modifications that cause misclassification with high confidence. However, existing detection methods either require expensive retraining, modify network architecture, or degrade performance on clean inputs. Here we show that adversarial perturbations create immediate, detectable entropy signatures in CNN activations that can be monitored without any model modification. Using parallel entropy monitoring on VGG-16, we demonstrate that adversarial inputs consistently shift activation entropy by 7% in early convolutional layers, enabling 90% detection accuracy with false positives and false negative rates below 20%. The complete separation between clean and adversarial entropy distributions reveals that CNNs inherently encode distribution shifts in their activation patterns. This work establishes that CNN reliability can be assessed through activation entropy alone, enabling practical deployment of self-diagnostic vision systems that detect adversarial inputs in real-time without compromising original model performance.