code for "Feature Importance-aware Transferable Adversarial Attacks"

Last update: Nov 24, 2022

Related tags

Deep Learning FIA

Overview

Feature Importance-aware Attack(FIA)

This repository contains the code for the paper:

Feature Importance-aware Transferable Adversarial Attacks (ICCV 2021)

Requirements

Python 3.6.8
Keras 2.2.4
Tensorflow 1.14.0
Numpy 1.16.2
Pillow 6.0.0
Scipy 1.2.1

Experiments

Introduction

attack.py : the implementation for different attacks.
verify.py : the code for evaluating generated adversarial examples on different models.

You should download the pretrained models from ( https://github.com/tensorflow/models/tree/master/research/slim, https://github.com/tensorflow/models/tree/archive/research/adv_imagenet_models) before running the code. Then place these model checkpoint files in ./models_tf.

Example Usage

Generate adversarial examples:

python attack.py --model_name vgg_16 --attack_method FIA --layer_name vgg_16/conv3/conv3_3/Relu --ens 30 --probb 0.7 --output_dir ./adv/FIA/

PIM:

python attack.py --model_name vgg_16 --attack_method PIM --amplification_factor 10 --gamma 1 --Pkern_size 3 --output_dir ./adv/PIM/

FIA+PIDIM

python attack.py --model_name vgg_16 --attack_method FIAPIM --layer_name vgg_16/conv3/conv3_3/Relu --ens 30 --probb 0.7 --amplification_factor 2.5 --gamma 0.5 --Pkern_size 3 --image_size 224 --image_resize 250 --prob 0.7 --output_dir ./adv/FIAPIDIM/

Different attack methods have different parameter setting, and the detailed setting can be found in our paper.

Evaluate the attack success rate

python verify.py --ori_path ./dataset/images/ --adv_path ./adv/FIA/ --output_file ./log.csv

Citing this work

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

@article{wang2021feature,
  title={Feature Importance-aware Transferable Adversarial Attacks},
  author={Wang, Zhibo and Guo, Hengchang and Zhang, Zhifei and Liu, Wenxin and Qin, Zhan and Ren, Kui},
  journal={arXiv preprint arXiv:2107.14185},
  year={2021}
}

code for "Feature Importance-aware Transferable Adversarial Attacks"

Related tags

Overview

Feature Importance-aware Attack(FIA)

Requirements

Experiments

Introduction

Example Usage

Generate adversarial examples:

Evaluate the attack success rate

Citing this work

Owner

Hengchang Guo

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Chinese Advertisement Board Identification(Pytorch)

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Code and data of the Fine-Grained R2R Dataset proposed in paper Sub-Instruction Aware Vision-and-Language Navigation

This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

Pytorch implementation of DeepMind's differentiable neural computer paper.

GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning

A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Header-only library for using Keras models in C++.

A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning.

An index of algorithms for learning causality with data

《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

SeqAttack: a framework for adversarial attacks on token classification models

Facilitates implementing deep neural-network backbones, data augmentations

Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Food Drinks and groceries Images Multi Lingual (FooDI-ML) dataset.

Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation".

CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped

Implementation of Axial attention - attending to multi-dimensional data efficiently