LAFEAT attack

Paper

This is the official repository for our paper "LAFEAT: Piercing Through Adversarial Defenses with Latent Features". The paper is available on:

• CVPR 2021 Open Access
• ArXiv

Please feel free to cite our paper with the following bibtex entry:

@InProceedings{Yu_2021_CVPR,
    author    = {Yu, Yunrui and Gao, Xitong and Xu, Cheng-Zhong},
    title     = {{LAFEAT}: Piercing Through Adversarial Defenses With Latent Features},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {5735-5745}
}

Introduction

We introduce LAFEAT, a unified $\ell^\infty$-norm white-box attack algorithm which harnesses latent features in its gradient descent steps. Our results show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.

Requirements

• Python 3 (>= 3.6)
• PyTorch (>= 1.2.0)

Instructions for reproducing attacks on TRADES

Note that for reproducibility, the scripts are made to be completely deterministic, your runs should hopefully produce exactly the same results as ours.

1. Download the original TRADES CIFAR-10 model_cifar_wrn.pt model provided by the authors, and place it in the models/ folder.

2. To train logits for intermediate features, run the following command:

   python3 train.py --max-epoch=100 --save-model=trades_new

   It will run for 100 epochs and save the final logits model at models/trades_new.pt. We have also included trained logits named models/trades.pt with the code, so you can skip this step.

3. To perform a multi-targeted attack on the TRADES model with trained intermediate logits, run:

   python3 attack.py \
       --verbose --batch-size=${your_batch_size:-2000} \
       --multi-targeted --num-iterations=1000 \
       --logits-model=models/trades_new.pt  # your trained logits

   It will run a multi-targeted LAFEAT attack and save the adversarial images at attacks/lafeat.{additional_info}.pt.

4. For testing with the original TRADES evaluation script, we need to first convert the adversarial examples for their script with the following command:

   python3 convert.py --name=lafeat.{additional_info}.pt

   By default, it converts the .pt file to a cifar10_X_adv.npy file and performs additional range clipping to ensure correct L-inf boundaries under the effect of floating-point errors. It also generates a new attacks/cifar10_X_adv.npy file. We ran multi-targeted LAFEAT with 1000 iterations, and generated the adversarial examples with a 52.94% accuracy for the CIFAR-10 test set, which places it at the top of the TRADES CIFAR-10 white-box leaderboard. For convenience, we uploaded the file anonymously, and you can download it from:

   • cifar10_X_adv.npy.

5. Download the CIFAR-10 datasets for TRADES’s testing script, and place them in the attacks/ folder:

   • cifar10_X.npy
   • cifar10_Y.npy

6. Evaluate with the original TRADES script (with minor modifications to make it work with our paths) using:

   python3 eval_trades.py

   and you should be able to test the accuracy of LAFEAT adversarial examples on the TRADES model.