P2FA

This repository contains the PyTorch code for the paper: Pixel2Feature Attack (P2FA): Rethinking the Perturbed Space to Enhance Adversarial Transferability(ICML 2025)

Datasets

1000 images for the NIPS 2017 adversarial competition.

directory/
├── 0.png
├── 1.png
├── ...
└── 999.jpg

normalization:

import torchvision.transforms as T

transforms = T.Compose([
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

Pre-trained Models

Models	Packages	Accuracy (%)
Inception-v3 (Inc-v3)	Pytorch	95.4
Inception-v4 (Inc-v4)	timm	94.8
Inception-ResNet-v2 (IncRes-v2 )	timm	97.2
ResNet-50 (Res-50)	Pytorch	91.8
ResNet-152 (Res-152)	Pytorch	93.6
VGG-16 (Vgg-16)	Pytorch	85.5
VGG-19 (Vgg-19)	Pytorch	87.5
Adv-Inception-v3 (Adv-Inc-v3)	timm	86.8
Ens-Adv-Inception-ResNet-v2 (Ens-IncRes-v2)	timm	94.5

P2FA

python main.py

Citation

If you find the idea or code useful for your research, please consider citing our paper:

@inproceedings{liupixel2feature,
  title={Pixel2Feature Attack (P2FA): Rethinking the Perturbed Space to Enhance Adversarial Transferability},
  author={Liu, Renpu and Wu, Hao and Zhang, Jiawei and Cheng, Xin and Luo, Xiangyang and Ma, Bin and Wang, Jinwei},
  booktitle={Forty-second International Conference on Machine Learning}
}