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

A DeepStack custom model for detecting common objects in dark/night images and videos.

Husein pet projects in here!

Implementing DropPath/StochasticDepth in PyTorch

Implementation for "Domain-Specific Bias Filtering for Single Labeled Domain Generalization"

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

The code is the training example of AAAI2022 Security AI Challenger Program Phase 8: Data Centric Robot Learning on ML models.

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

AI创造营：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Code for One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)

Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning"

Contains a bunch of different python programm tasks

A crossplatform menu bar application using mpv as DLNA Media Renderer.

The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

Data and analysis code for an MS on SK VOC genomes phenotyping/neutralisation assays

Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

Contrastive Fact Verification

The official GitHub repository for the Argoverse 2 dataset.

A Unified Framework and Analysis for Structured Knowledge Grounding

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

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

A DeepStack custom model for detecting common objects in dark/night images and videos.

Husein pet projects in here!

Implementing DropPath/StochasticDepth in PyTorch

Implementation for "Domain-Specific Bias Filtering for Single Labeled Domain Generalization"

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

The code is the training example of AAAI2022 Security AI Challenger Program Phase 8: Data Centric Robot Learning on ML models.

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Code for One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)

Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning"

Contains a bunch of different python programm tasks

A crossplatform menu bar application using mpv as DLNA Media Renderer.

The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

Data and analysis code for an MS on SK VOC genomes phenotyping/neutralisation assays

Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

Contrastive Fact Verification

The official GitHub repository for the Argoverse 2 dataset.

A Unified Framework and Analysis for Structured Knowledge Grounding

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

AI创造营：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人