About
PyTorch code for our paper:"On Success and Simplicity: A Second Look at Transferable Targeted Attacks".
Zhengyu Zhao, Zhuoran Liu, Martha Larson. NeurIPS 2021.
TL;DR: We demonstrate that the conventional simple, iterative attacks can actually achieve even higher targeted transferability than the current SOTA, resource-intensive attacks. The key is to use enough iterations for ensuring convergence and to replace the widely-used Cross-Entropy loss with a simpler Logit loss for preventing the decreasing gradient problem.
Requirements
torch>=1.7.0; torchvision>=0.8.1; tqdm>=4.31.1; pillow>=7.0.0; matplotlib>=3.2.2; numpy>=1.18.1;
Dataset
The 1000 images from the NIPS 2017 ImageNet-Compatible dataset are provided in the folder dataset/images
, along with their metadata in dataset/images.csv
. More details about this dataset can be found in its official repository.
Evaluation
We evaluated three simple transferable targeted attacks (CE, Po+Trip, and Logit) in the following six transfer scenarios. If not mentioned specifically, all attacks are integrated with TI, MI, and DI, and run with 300 iterations to ensure convergence. L∞=16 is applied.