linfengxu / targeted-transfer

Simple yet effective targeted transfer attack (NeurIPS 2021)

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.

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.

eval_single.py: Single-model transfer.

eval_ensemble.py: Ensemble transfer.

eval_low_ranked.py: Transfer with low-ranked targets.

eval_10_targets.py: "10-Targets (all-source)" setting to compare with the resource-intensive SOTA, TTP, which is based on training target-class-specific GANs.

eval_unbounded.py: Undounded setting to the resource-intensive FDA^(N)+xent, which is based on training target-class-specific auxiliary binary classifiers.

eval_tUAP.py: Generating targeted UAPs.

Attacking the Google Cloud Vision API.