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

Teaching end to end workflow of deep learning

🕺Full body detection and tracking

Adapter-BERT: Parameter-Efficient Transfer Learning for NLP.

[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

PyTorch ,ONNX and TensorRT implementation of YOLOv4

A curated list and survey of awesome Vision Transformers.

SemEval2022 Patronizing and Condescending Language (PCL) Detection

This repository contains the implementation of the paper: Federated Distillation of Natural Language Understanding with Confident Sinkhorns

CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

CountDown to New Year and shoot fireworks

GLM (General Language Model)

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

PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

A framework for joint super-resolution and image synthesis, without requiring real training data

Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network

Amazing-Python-Scripts - 🚀 Curated collection of Amazing Python scripts from Basics to Advance with automation task scripts.

Fast Neural Representations for Direct Volume Rendering

This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification