探索通过载荷注入和FGSM攻击对ResNet-50的安全机器学习
Exploring Secure Machine Learning Through Payload Injection and FGSM Attacks on ResNet-50
AI总结：
本研究通过FGSM对抗攻击和载荷注入测试ResNet-50，发现其准确率不变但置信度异常，且注入成功率高，揭示模型脆弱性并强调防御需求。
摘要：
本文研究了ResNet-50图像分类模型在两种突出安全威胁下的韧性：快速梯度符号法（FGSM）对抗攻击和恶意载荷注入。最初，模型在干净图像上达到53.33%的准确率。当受到FGSM扰动时，其整体准确率保持不变；然而，模型对错误预测的置信度显著增加。同时，载荷注入方案在93.33%的测试样本中成功执行，揭示了隐蔽攻击如何在不降低视觉质量的情况下操纵模型预测。这些发现强调了即使是高性能神经网络也存在脆弱性，并凸显了为安全关键应用开发更鲁棒防御机制的紧迫性。
英文摘要：
This paper investigates the resilience of a ResNet-50 image classification model under two prominent security threats: Fast Gradient Sign Method (FGSM) adversarial attacks and malicious payload injection. Initially, the model attains a 53.33% accuracy on clean images. When subjected to FGSM perturbations, its overall accuracy remains unchanged; however, the model's confidence in incorrect predictions notably increases. Concurrently, a payload injection scheme is successfully executed in 93.33% of the tested samples, revealing how stealthy attacks can manipulate model predictions without degrading visual quality. These findings underscore the vulnerability of even high-performing neural networks and highlight the urgency of developing more robust defense mechanisms for security-critical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
