Deep neural networks (DNNs) have significantly advanced various domains, but
their vulnerability to adversarial attacks poses serious concerns.
Understanding these vulnerabilities and developing effective defense mechanisms
is crucial. DeepFool, an algorithm proposed by Moosavi-Dezfooli et al. (2016),
finds minimal perturbations to misclassify input images. However, DeepFool
lacks a targeted approach, making it less effective in specific attack
scenarios. Also, in previous related works, researchers primarily focus on
success, not considering how much an image is getting distorted; the integrity
of the image quality, and the confidence level to misclassifying. So, in this
paper, we propose Targeted DeepFool, an augmented version of DeepFool that
allows targeting specific classes for misclassification. We also introduce a
minimum confidence score requirement hyperparameter to enhance flexibility. Our
experiments demonstrate the effectiveness and efficiency of the proposed method
across different deep neural network architectures while preserving image
integrity as much as possible. Results show that one of the deep convolutional
neural network architectures, AlexNet, and one of the state-of-the-art model
Vision Transformer exhibit high robustness to getting fooled. Our code will be
made public when publishing the paper.

通过提出 “Targeted DeepFool” 算法，该研究通过在深度神经网络上训练特定的攻击样本来应对对抗攻击，以提高图像质量的完整性和准确性。

使用 DeepFool 算法定制深度神经网络的针对性类别操控的对抗攻击

Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class  Manipulation Using DeepFool Algorithm

We propose a new ensemble method for detecting and classifying adversarial
examples generated by state-of-the-art attacks, including DeepFool and C&W. Our
method works by training the members of an ensemble to have low classification
error on random benign examples while simultaneously minimizing agreement on
examples outside the training distribution. We evaluate on both MNIST and
CIFAR-10, against oblivious and both white- and black-box adversaries.

提出了一种新的集成方法，用于检测和分类最新攻击算法生成的对抗样本，包括 DeepFool 和 C&W，该方法通过训练集成成员对随机良性样本的分类误差较低同时在训练分布之外的样本上最小化一致性来工作。结果表明该方法能够抵抗白盒和黑盒攻击，并且在 MNIST 数据集上表现良好。