(ECCV2024) Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection

This repository is the official implementation of our paper Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection.

Demos on GPT-4

More demos on GPT-4 can be found in our paper.

Overview

Motivation: Traditional targeted attacks are designed to mislead a model to recognize an image as a specific known target class, which restricts their generalization to unknown classes. To address this issue, we the paradigm of arbitrary-target attack, which can attack any target after one training.

Specifically, we propose Generalized Adversarial attacKER (GAKer), which is able to construct adversarial examples to any target class. The core idea behind GAKer is to craft a latently infected representation during adversarial example generation.

Requirements

1. create environment

conda create -n gaker python==3.8
conda activate gaker

conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.1 -c pytorch -c conda-forge 

pip install ....

2. Datasets:

Source images dataset : Just download ImageNet Dataset

Target images dataset : We select 200 classes from ImageNet Dataset. For each classes, we choose 325 pictures.The format is consistent with ImageNet, for example, target_images_dataset/n01440764/1.png.

Generalized Adversarial attacKER (GAKer)

1. step1: train the generator

CUDA_VISIBLE_DEVICES=0 python GAKER.py --batch_size 32 --Source_Model ResNet50 --epoch 20 --state train_model --Generator_save_dir 'save_model/'

2. step2: craft adversarial examples and evaluate

CUDA_VISIBLE_DEVICES=0 python GAKER.py --Source_Model ResNet50 --test_load_weight ckpt_19_ResNet50_.pt --state craftadv --Generator_save_dir './save_model/' --ran_best random --set_targets targets_200_cossimilar --target_select 1

Results

Citing this work

If you find this work is useful in your research, please consider citing:

@inproceedings{sun2024targetoffenseadversarialexample,
      title={Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection}, 
      author={Youheng Sun and Shengming Yuan and Xuanhan Wang and Lianli Gao and Jingkuan Song},
      year={2024},
Booktitle = {ECCV},

}