TransferAttack

About

TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.

Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation.

We also release a list of papers about transfer-based attacks here.

Why TransferAttack

There are a lot of reasons for TransferAttack, such as:

• A benchmark for evaluating new transfer-based attacks: TransferAttack categorizes existing transfer-based attacks into several types and fairly evaluates various transfer-based attacks under the same setting.
• Evaluate the robustness of deep models: TransferAttack provides a plug-and-play interface to verify the robustness of models, such as CNNs and ViTs.
• A summary of transfer-based attacks: TransferAttack reviews numerous transfer-based attacks, making it easy to get the whole picture of transfer-based attacks for practitioners.

Citation

If our paper or this code is useful for your research, please cite our paper:

@article{wang2026devling,
     title={{Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation}},
     author={Xiaosen Wang and Zhijin Ge and Bohan Liu and Zheng Fang and Fengfan Zhou and Ruixuan Zhang and Shaokang Wang and Yuyang Luo},
     journal={arXiv preprint arXiv:2602.23117},
     year={2026}
}

Requirements

• Python >= 3.6
• PyTorch >= 1.12.1
• Torchvision >= 0.13.1
• timm >= 0.6.12

pip install -r requirements.txt

Usage

We adopt an academic-standard ImageNet-compatible dataset comprising 1,000 PNG images for our experiments. Download the data from or [ into /path/to/data. Then you can execute the attack as follows:

python main.py --input_dir ./path/to/data --output_dir adv_data/mifgsm/resnet50 --attack mifgsm --model=resnet50
python main.py --input_dir ./path/to/data --output_dir adv_data/mifgsm/resnet50 --eval

Attacks and Models

Untargeted Attacks