This technical report delves into an in-depth exploration of adversarial
attacks specifically targeted at Deep Neural Networks (DNNs) utilized for image
classification. The study also investigates defense mechanisms aimed at
bolstering the robustness of machine learning models. The research focuses on
comprehending the ramifications of two prominent attack methodologies: the Fast
Gradient Sign Method (FGSM) and the Carlini-Wagner (CW) approach. These attacks
are examined concerning three pre-trained image classifiers: Resnext50_32x4d,
DenseNet-201, and VGG-19, utilizing the Tiny-ImageNet dataset. Furthermore, the
study proposes the robustness of defensive distillation as a defense mechanism
to counter FGSM and CW attacks. This defense mechanism is evaluated using the
CIFAR-10 dataset, where CNN models, specifically resnet101 and Resnext50_32x4d,
serve as the teacher and student models, respectively. The proposed defensive
distillation model exhibits effectiveness in thwarting attacks such as FGSM.
However, it is noted to remain susceptible to more sophisticated techniques
like the CW attack. The document presents a meticulous validation of the
proposed scheme. It provides detailed and comprehensive results, elucidating
the efficacy and limitations of the defense mechanisms employed. Through
rigorous experimentation and analysis, the study offers insights into the
dynamics of adversarial attacks on DNNs, as well as the effectiveness of
defensive strategies in mitigating their impact.

这篇学术论文深入探讨了针对图像分类任务中使用的深度神经网络（DNN）的对抗性攻击，并调查了旨在增强机器学习模型鲁棒性的防御机制。研究聚焦于理解两种主要攻击方法的影响：快速梯度符号法（FGSM）和 Carlini-Wagner（CW）方法。该文献提出防御蒸馏作为一种抵御 FGSM 和 CW 攻击的防御机制，并使用 CIFAR-10 数据集对其进行评估。虽然该提出的防御技术在抵御 FGSM 等攻击方面表现出有效性，但依然易受到更复杂的技术，如 CW 攻击的影响。通过严格的实验和分析，该研究为对 DNNs 的对抗性攻击的动态以及防御策略的有效性提供了深入见解。