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

Related tags

Deep Learningadversarial-attacks
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

OPTML Group 2 Oct 5, 2022
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

OPTML Group 2 Oct 5, 2022
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

null 32 Dec 26, 2022
[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

Microsoft 22 Sep 15, 2022
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

Lei Huang 21 Dec 27, 2022
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

Phil Wang 59 Nov 24, 2022
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

Peidong Liu(刘沛东) 54 Dec 17, 2022
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

Haotong Qin 59 Dec 17, 2022
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

FedML-AI 175 Dec 1, 2022
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
GitHub Repository
Deep learned, hardware-accelerated 3D object pose estimation

Isaac ROS Pose Estimation Overview This repository provides NVIDIA GPU-accelerated packages for 3D object pose estimation. Using a deep learned pose e

NVIDIA Isaac ROS 41 Dec 18, 2022
Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Orange Chicken: Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation This repository contains code and data f

Zoey Liu 0 Jan 07, 2022
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX: Autograd and XLA Quickstart | Transformations | Install guide | Neural net libraries | Change logs | Reference docs | Code search News: JAX tops

Google 21.3k Jan 01, 2023
The InterScript dataset contains interactive user feedback on scripts generated by a T5-XXL model.

Interscript The Interscript dataset contains interactive user feedback on a T5-11B model generated scripts. Dataset data.json contains the data in an

AI2 8 Dec 01, 2022
Face uncertainty quantification or estimation using PyTorch.

Face-uncertainty-pytorch This is a demo code of face uncertainty quantification or estimation using PyTorch. The uncertainty of face recognition is af

Kaen 3 Sep 16, 2022
Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022) Prerequisite PyTorch = 1.2.0 P

16 Dec 14, 2022
Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022
Generalized Decision Transformer for Offline Hindsight Information Matching

Generalized Decision Transformer for Offline Hindsight Information Matching [arxiv] If you use this codebase for your research, please cite the paper:

Hiroki Furuta 35 Dec 12, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Dec 31, 2022
MAVE: : A Product Dataset for Multi-source Attribute Value Extraction

MAVE: : A Product Dataset for Multi-source Attribute Value Extraction The dataset contains 3 million attribute-value annotations across 1257 unique ca

Google Research Datasets 89 Jan 08, 2023
TAUFE: Task-Agnostic Undesirable Feature DeactivationUsing Out-of-Distribution Data

A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power. However, its prediction can be easily biased to undesirable features, whi

KAIST Data Mining Lab 8 Dec 07, 2022
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
PyDeepFakeDet is an integrated and scalable tool for Deepfake detection.

PyDeepFakeDet An integrated and scalable library for Deepfake detection research. Introduction PyDeepFakeDet is an integrated and scalable Deepfake de

Junke, Wang 49 Dec 11, 2022
Training data extraction on GPT-2

Training data extraction from GPT-2 This repository contains code for extracting training data from GPT-2, following the approach outlined in the foll

Florian Tramer 62 Dec 07, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph

75 Dec 22, 2022
This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Yogi-Optimizer_Keras This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras) The NeurIPS-Paper can be found here: http://papers.nips.c

14 Sep 13, 2022
City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

City Seeds This is a random generator of cultural characteristics intended to sp

Aydin O'Leary 2 Mar 12, 2022