Furthermore, previous research has predominantly concentrated on the transferability issues of non-targeted attacks, whereas enhancing the transferability of targeted adversarial examples presents even greater challenges. Traditional attack techniques typically employ cross-entropy as a loss measure, iteratively ...
Various transfer attack methods have been proposed to evaluate the robustness of deep neural networks (DNNs). Although manifesting remarkable performance in generating untargeted adversarial perturbations, existing proposals still fail to achieve high targeted transferability. In this work, we discover that ...
Below we provide running commands for generating targeted adversarial examples on ImageNet NeurIPS validation set (1k images) under our single-class setting (take class id 150 as example): python eval.py --data_dir data/ImageNet1k/ --model_type incv3 --load_path $SAVE_CHECKPOINT --save_dir...
adversarial dataset error rate(error)错误率是 D_{adv} 中f_{b}(x_{adv})\ne y_{true} 样本数的百分比 The untargeted transfer rate(uTR)定义D_{uTR} \subseteq D_{adv} ,包含被 f_{w} 误分类的元素 The targeted success rate(tSuc) 代表在 D_{adv} 中f_{b}(x_{adv})=y_{target} 的...
Making Adversarial Examples More Transferable and Indistinguishable (AAAI2022) Boosting Adversarial Transferability by Achieving Flat Local Maxima (NeurIPS 2023) Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients (ICCV 2023) ...
Results on MNIST, CIFAR10, and ImageNet show that even at a low 1000 query budget, we still achieve high attack success rates in both targeted and untargeted attacks, and the query efficiency is dozens of times higher than the previous state-of-the-art attack methods. Furthermore, we show...
Targeted attack and non-targeted attack experiments demonstrated that the proposed method can generate high-quality, transferable, robust, private face images with only minor perturbations more effectively than other existing methods.doi:10.1007/S40747-021-00399-6Jingjing Yang...
showed that adversarial examples can be explained by features of the attacked class label. In our targeted attack case, we wish to imprint the features of the target class distribution onto the source samples within an allowed distance. However, black-box (unknown) model might apply different set...
evaluation_modeis the evaluation mode of the attack (0:targeted ,1:untargeted) Other parameters can be founded in the script, or runpython attack.py -h. The default parameters are the ones used in the paper. The results will be saved inresults/exp0/with the original point cloud and atta...
For reproducing the result of un-targeted, you can run the code using the The results can be reproduced using the following command: Attack defense model: Please download the weight of the Imagenet model fromhttps://drive.google.com/file/d/1nNRhzijZnHjHJ6SkFVTaFxDO-YnxiAhZ/view?usp=shari...