davidjinzb / danaa

Implementation of DANAA: Double Adversarial Neuron Attribution for Transferable Attacks

This repository contains the official implementation code for the research paper DANAA: Towards Transferable Attacks with Double Adversarial Neuron Attribution. The DANAA framework is designed to generate adversarial examples with enhanced transferability by leveraging the concept of double adversarial neuron attribution.

Prerequisites

The implementation requires the following software and libraries:

• Python 3.6.13
• Keras 2.2.4
• NumPy 1.16.2
• TensorFlow 1.14.0
• TQDM 4.63.1
• Pillow 6.0.0
• SciPy 1.2.1

Pre-trained Models

For the experiments, pre-trained models are employed. The following table lists the required models along with their download links:

Model	Source
Inception v3	Download
Inception v4	Download
Inception-ResNet-v2	Download
ResNet v2 152	Download
Inception v3 adv	Download
Inception ResNet v2 adv	Download
Inception v3 adv ens3	Download
Inception v3 adv ens4	Download
Inception ResNet v2 adv ens3	Download

The non-adversarial models are sourced from TensorFlow's Slim Model Library, while the adversarially trained models can be found in the Adversarial ImageNet Models Repository. Download and place these models in the models directory within the repository.

Execution Instructions

Running DANAA Attacks

To execute the DANAA attack method, use the following command format, adjusting parameters as necessary for the desired model and settings:

python DANAA.py --model_name <model> --attack_method DANAA --layer_name <layer> --ens 30 --output_dir ./outputs/DANAA/ --scale 0.25

For instance, to run DANAA on the Inception V3 model, the command would be:

python DANAA.py --model_name inception_v3 --attack_method DANAA --layer_name InceptionV3/InceptionV3/Mixed_5b/concat --ens 30 --output_dir ./outputs/DANAA/ --scale 0.25

Additional Attack Methods

The framework also supports comparison with other attack methodologies such as NAA, MIM, NRDM, FIA, and FDA. To execute these, simply replace the --attack_method parameter with the desired attack type.

Verification

To verify the effectiveness of the generated adversarial examples, use the verify.py script as follows:

python verify.py --ori_path ./dataset/images/ --output_dir ./outputs/DANAA/

Citing DANAA

For academic use, please cite the following paper.

@inproceedings{jin2023danaa,
  title={DANAA: Towards transferable attacks with double adversarial neuron attribution},
  author={Jin, Zhibo and Zhu, Zhiyu and Wang, Xinyi and Zhang, Jiayu and Shen, Jun and Chen, Huaming},
  booktitle={International Conference on Advanced Data Mining and Applications},
  pages={456--470},
  year={2023},
  organization={Springer}
}

Refenece

Code refer to: NAA.