Meta Gradient Adversarial Attack

This repository is an official implementation of the paper Meta Gradient Adversarial Attack, which has been accepted by ICCV2021.

Method

Abstract

In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called Meta Gradient Adversarial Attack (MGAA), which is plug-and-play and can be integrated with any existing gradient-based attack method for improving the cross-model transferability. Specifically, we randomly sample multiple models from a model zoo to compose different tasks and iteratively simulate a white-box attack and a black-box attack in each task. By narrowing the gap between the gradient directions in white-box and black-box attacks, the transferability of adversarial examples on the black-box setting can be improved. Extensive experiments on the CIFAR10 and ImageNet datasets show that our architecture outperforms the state-of-the-art methods for both black-box and white-box attack settings.

Usage

Environment

GPU: single GTX 1080Ti
python: 3.6.5
tensorflow: 1.9.0
scipy: 1.1.0
numpy: 1.14.3
pandas: 1.1.5

Model

We conduct the experiments with the widely used models trained on ImageNet.

For all the natural trained model, you can download them from here.

For all the adversarial trained model, you can download them from here.

After downloading all the model, please put them under the directory of model.

For some models, for the convenience of coding, we have made some adjustments to the variable names. In order to implement these changes, you can use the file misc.py.

• For pnasnet-5_mobile_model_modify.ckpt, we add variable scope of pnasnet_mobile before all variable names.
• For nasnet-a_mobile_model_modify.ckpt, we add variable scope of nasnet_mobile before all variable names.
• For mobilenet_v2_1.4_224_modify.ckpt, we replace variable scope of MobilenetV2 with MobilenetV2_1.4 at the beginning of the variable names.
• For adv_inception_v3_rename.ckpt, we replace variable scope of InceptionV3 with AdvInceptionV3 at the beginning of the variable names.
• For ens3_adv_inception_v3_rename.ckpt, we replace variable scope of InceptionV3 with Ens3AdvInceptionV3 at the beginning of the variable names.
• For ens4_adv_inception_v3_rename.ckpt, we replace variable scope of InceptionV3 with Ens4AdvInceptionV3 at the beginning of the variable names.
• For ens_adv_inception_resnet_v2_rename.ckpt, we replace variable scope of InceptionResnetV2 with EnsAdvInceptionResnetV2 at the beginning of the variable names.

Running

CUDA_VISIBLE_DEVICES=X bash run_attack.sh

The generated adversarial examples are saved under the directory of outputs, along with the evaluation results in simple_eval_XXX.log

Citation

If you use our work in your research, please consider citing

@article{yuan2021meta,
    title={Meta Gradient Adversarial Attack},
    author={Yuan, Zheng and Zhang, Jie and Jia, Yunpei and Tan, Chuanqi and Xue, Tao and Shan, Shiguang},
    journal={arXiv preprint arXiv:2108.04204},
    year={2021}
}