Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

Overview

Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022)

This is the Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains). In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks).

Requirement

  • Python 3.7
  • Pytorch 1.8.0
  • torchvision 0.9.0
  • numpy 1.20.2
  • scipy 1.7.0
  • pandas 1.3.0
  • opencv-python 4.5.2.54
  • joblib 0.14.1
  • Pillow 6.1

Dataset

images

  • Download the ImageNet training dataset.

  • Download the testing dataset.

Note: After downloading CUB-200-2011, Standford Cars and FGVC Aircraft, you should set the "self.rawdata_root" (DCL_finegrained/config.py: lines 59-75) to your saved path.

Target model

The checkpoint of target model should be put into model folder.

  • CUB-200-2011, Stanford Cars and FGVC AirCraft can be downloaded from here.
  • CIFAR-10, CIFAR-100, STL-10 and SVHN can be automatically downloaded.
  • ImageNet pre-trained models are available at torchvision.

Pretrained-Generators

framework Adversarial generators are trained against following four ImageNet pre-trained models.

  • VGG19
  • VGG16
  • ResNet152
  • DenseNet169

After finishing training, the resulting generator will be put into saved_models folder. You can also download our pretrained-generator from here.

Train

Train the generator using vanilla BIA (RN: False, DA: False)

python train.py --model_type vgg16 --train_dir your_imagenet_path --RN False --DA False

your_imagenet_path is the path where you download the imagenet training set.

Evaluation

Evaluate the performance of vanilla BIA (RN: False, DA: False)

python eval.py --model_type vgg16 --RN False --DA False

Citing this work

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

@inproceedings{Zhang2022BIA,
  author    = {Qilong Zhang and
               Xiaodan Li and
               Yuefeng Chen and
               Jingkuan Song and
               Lianli Gao and
               Yuan He and
               Hui Xue},
  title     = {Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains},
  Booktitle = {International Conference on Learning Representations},
  year      = {2022}
}

Acknowledge

Thank @aaron-xichen and @Muzammal-Naseer for sharing their codes.

You might also like...
This repository contains the code and models necessary to replicate the results of paper:  How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

This repository contains the code and models necessary to replicate the results of paper:  How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators

AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi

Iterative Normalization: Beyond Standardization towards Efficient Whitening

IterNorm Code for reproducing the results in the following paper: Iterative Normalization: Beyond Standardization towards Efficient Whitening Lei Huan

Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021
Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Geometric Vector Perceptron Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biom

Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

Comments
  • About the comparative methods

    About the comparative methods

    Thank you for your insightful work! In Table3, I want to know that how to perform PGD or DIM on CUB with source models pretrained on ImageNet. Thank you~

    opened by lwmming 6
  • cursor already registered in Tk_GetCursor Aborted (core dumped)

    cursor already registered in Tk_GetCursor Aborted (core dumped)

    python train.py --model_type vgg16 --RN False --DA False

    I tried the above default training, but the error occurred at the end of the batch (epoch 1) training. Can you help debug this please?

    opened by hoonsyang 2
  • missing file

    missing file

    https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/7e8b1b8ec5728ebc01723f2c444bf2d5275ee7be/DCL_finegrained/LoadModel.py#L6 NameError: name 'pretrainedmodels' is not defined`

    opened by nkv1995 2
  • when computing cosine similarity

    when computing cosine similarity

    Hi! this is more of a question for the elegant work you have here but less of an issue.

    So when you take cosine similarity (which is to be decreased during training) between two feature maps, you take,

    loss = torch.cosine_similarity((adv_out_slice*attention).reshape(adv_out_slice.shape[0], -1), 
                                (img_out_slice*attention).reshape(img_out_slice.shape[0], -1)).mean()
    

    and that's to compare two flatten vectors, each of which is the flattened feature maps of size (N feature channels, width, height).

    I wonder why not comparing the flattened feature maps with respect to each channel, and then take the average across channels? To me, you're comparing two vectors that are (Nwidthheight)-dimensional, which is not so straightforward to me. Thanks in advance for any intuition behind!

    opened by juliuswang0728 1
Releases(pretrained_models)
Owner
Alibaba-AAIG
Alibaba Artificial Intelligence Governance Laboratory
Alibaba-AAIG
Node-level Graph Regression with Deep Gaussian Process Models

Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python

1 Jan 16, 2022
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

Jacobi(Jiabo He) 147 Dec 05, 2022
Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight)

UPDeT Official Implementation of UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers (ICLR 2021 spotlight) The

hhhusiyi 96 Dec 22, 2022
Uni-Fold: Training your own deep protein-folding models.

Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi

DeepModeling 88 Jan 03, 2023
WaveFake: A Data Set to Facilitate Audio DeepFake Detection

WaveFake: A Data Set to Facilitate Audio DeepFake Detection This is the code repository for our NeurIPS 2021 (Track on Datasets and Benchmarks) paper

Chair for Sys­tems Se­cu­ri­ty 27 Dec 22, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
An efficient framework for reinforcement learning.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

16 Nov 30, 2022
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

8 Nov 08, 2022
UCSD Oasis platform

oasis UCSD Oasis platform Local project setup Install Docker Compose and make sure you have Pip installed Clone the project and go to the project fold

InSTEDD 4 Jun 16, 2021
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
This repo provides function call to track multi-objects in videos

Custom Object Tracking Introduction This repo provides function call to track multi-objects in videos with a given trained object detection model and

Jeff Lo 51 Nov 22, 2022
Score refinement for confidence-based 3D multi-object tracking

Score refinement for confidence-based 3D multi-object tracking Our video gives a brief explanation of our Method. This is the official code for the pa

Cognitive Systems Research Group 47 Dec 26, 2022
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
Code for paper [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot] (ICCV 2021, oral))

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot This repository is the official PyTorch implementation of ICCV-21 pape

Jiarui 21 May 09, 2022
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Coming soon!

ToxiChat Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Install depen

Ashutosh Baheti 11 Jan 01, 2023
A curated list of awesome Model-Based RL resources

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

OpenDILab 427 Jan 03, 2023
We envision models that are pre-trained on a vast range of domain-relevant tasks to become key for molecule property prediction

We envision models that are pre-trained on a vast range of domain-relevant tasks to become key for molecule property prediction. This repository aims to give easy access to state-of-the-art pre-train

GMUM 90 Jan 08, 2023
Working demo of the Multi-class and Anomaly classification model using the CLIP feature space

👁️ Hindsight AI: Crime Classification With Clip About For Educational Purposes Only This is a recursive neural net trained to classify specific crime

Miles Tweed 2 Jun 05, 2022
LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs

LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs This is the code for the LERP. Dataset The dataset used is MI

5 Jun 18, 2022