[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Overview

Unlearnable Examples

Code for ICLR2021 Spotlight Paper "Unlearnable Examples: Making Personal Data Unexploitable " by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang.

Quick Start

Use the QuickStart.ipynb notebook for a quick start.

In the notebook, you can find the minimal implementation for generating sample-wise unlearnable examples on CIFAR-10. Please remove mlconfig from models/__init__.py if you are only using the notebook and copy-paste the model to the notebook.

Experiments in the paper.

Check scripts folder for *.sh for each corresponding experiments.

Sample-wise noise for unlearnable example on CIFAR-10

Generate noise for unlearnable examples
python3 perturbation.py --config_path             configs/cifar10                \
                        --exp_name                path/to/your/experiment/folder \
                        --version                 resnet18                       \
                        --train_data_type         CIFAR-10                       \
                        --noise_shape             50000 3 32 32                  \
                        --epsilon                 8                              \
                        --num_steps               20                             \
                        --step_size               0.8                            \
                        --attack_type             min-min                        \
                        --perturb_type            samplewise                      \
                        --universal_stop_error    0.01
Train on unlearnable examples and eval on clean test
python3 -u main.py    --version                 resnet18                       \
                      --exp_name                path/to/your/experiment/folder \
                      --config_path             configs/cifar10                \
                      --train_data_type         PoisonCIFAR10                  \
                      --poison_rate             1.0                            \
                      --perturb_type            samplewise                      \
                      --perturb_tensor_filepath path/to/your/experiment/folder/perturbation.pt \
                      --train

Class-wise noise for unlearnable example on CIFAR-10

Generate noise for unlearnable examples
python3 perturbation.py --config_path             configs/cifar10                \
                        --exp_name                path/to/your/experiment/folder \
                        --version                 resnet18                       \
                        --train_data_type         CIFAR-10                       \
                        --noise_shape             10 3 32 32                     \
                        --epsilon                 8                              \
                        --num_steps               1                              \
                        --step_size               0.8                            \
                        --attack_type             min-min                        \
                        --perturb_type            classwise                      \
                        --universal_train_target  'train_subset'                 \
                        --universal_stop_error    0.1                            \
                        --use_subset
Train on unlearnable examples and eval on clean test
python3 -u main.py    --version                 resnet18                       \
                      --exp_name                path/to/your/experiment/folder \
                      --config_path             configs/cifar10                \
                      --train_data_type         PoisonCIFAR10                  \
                      --poison_rate             1.0                            \
                      --perturb_type            classwise                      \
                      --perturb_tensor_filepath path/to/your/experiment/folder/perturbation.pt \
                      --train

Cite Our Work

@inproceedings{huang2021unlearnable,
    title={Unlearnable Examples: Making Personal Data Unexploitable},
    author={Hanxun Huang and Xingjun Ma and Sarah Monazam Erfani and James Bailey and Yisen Wang},
    booktitle={ICLR},
    year={2021}
}
Object Database for Super Mario Galaxy 1/2.

Super Mario Galaxy Object Database Welcome to the public object database for Super Mario Galaxy and Super Mario Galaxy 2. Here, we document all object

Aurum 9 Dec 04, 2022
Discord Multi Tool that focuses on design and easy usage

Multi-Tool-v1.0 Discord Multi Tool that focuses on design and easy usage Delete webhook Block all friends Spam webhook Modify webhook Webhook info Tok

Lodi#0001 24 May 23, 2022
Multi-robot collaborative exploration and mapping through Voronoi partition and DRL in unknown environment

Voronoi Multi_Robot Collaborate Exploration Introduction In the unknown environment, the cooperative exploration of multiple robots is completed by Vo

PeaceWord 6 Nov 22, 2022
Paper list of log-based anomaly detection

Paper list of log-based anomaly detection

Weibin Meng 411 Dec 05, 2022
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion.

OstrichRL This is the repository accompanying the paper OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion. It contain

Vittorio La Barbera 51 Nov 17, 2022
The `rtdl` library + The official implementation of the paper

The `rtdl` library + The official implementation of the paper "Revisiting Deep Learning Models for Tabular Data"

Yandex Research 510 Dec 30, 2022
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)

MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me

88 Jan 04, 2023
Realistic lighting in ursina!

Ursina Lighting Realistic lighting in ursina! If you want to have realistic lighting in ursina, import the UrsinaLighting.py in your project and use t

17 Jul 07, 2022
This repo is about to create the Streamlit application for given ML model.

HR-Attritiion-using-Streamlit This repo is about to create the Streamlit application for given ML model. Problem Statement: Managing peoples at workpl

Pavan Giri 0 Dec 10, 2021
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

jaxdf - JAX-based Discretization Framework Overview | Example | Installation | Documentation ⚠️ This library is still in development. Breaking changes

UCL Biomedical Ultrasound Group 65 Dec 23, 2022
ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection This repository contains implementation of the

Visual Understanding Lab @ Samsung AI Center Moscow 190 Dec 30, 2022
CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning CL-Gym is a small yet very flexible library for continual learning research and developme

Iman Mirzadeh 36 Dec 25, 2022
PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

185 Dec 26, 2022
Adversarial Attacks are Reversible via Natural Supervision

Adversarial Attacks are Reversible via Natural Supervision ICCV2021 Citation @InProceedings{Mao_2021_ICCV, author = {Mao, Chengzhi and Chiquier

Computer Vision Lab at Columbia University 20 May 22, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Relative Human dataset, CVPR 2022

Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including: Depth layers (DLs): relative depth relationsh

Yu Sun 112 Dec 02, 2022
Deeper insights into graph convolutional networks for semi-supervised learning

deeper_insights_into_GCNs Deeper insights into graph convolutional networks for semi-supervised learning References data and utils.py come from Implem

Davidham3 17 Dec 16, 2022
Deep Learning Visuals contains 215 unique images divided in 23 categories

Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with P

Daniel Voigt Godoy 1.3k Dec 28, 2022
Code for our CVPR 2022 Paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection"

GEN-VLKT Code for our CVPR 2022 paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection". Contributed by Yue Lia

Yue Liao 47 Dec 04, 2022
QMagFace: Simple and Accurate Quality-Aware Face Recognition

Quality-Aware Face Recognition 26.11.2021 start readme QMagFace: Simple and Accurate Quality-Aware Face Recognition Research Paper Implementation - To

Philipp Terhörst 59 Jan 04, 2023