[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Related tags

Deep LearningUnlearnable-Examples
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}
}
Owner
Hanxun Huang
Hanxun Huang
GitHub Repository https://hanxunh.github.io/Unlearnable-Examples/
Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data.

Deep Learning Dataset Maker Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data. How to use Down

deepbands 25 Dec 15, 2022
A PyTorch Lightning solution to training OpenAI's CLIP from scratch.

train-CLIP πŸ“Ž A PyTorch Lightning solution to training CLIP from scratch. Goal ⚽ Our aim is to create an easy to use Lightning implementation of OpenA

Cade Gordon 396 Dec 30, 2022
Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

ReLU-GP Residual (RGPR) This repository contains code for reproducing the following NeurIPS 2021 paper: @inproceedings{kristiadi2021infinite, title=

Agustinus Kristiadi 4 Dec 26, 2021
code from "Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity"

Code associated with the paper "Tensor decomposition of higher-order correlations by nonlinear Hebbian learning," Ocker & Buice, Neurips 2021. "plot_f

Gabriel Koch Ocker 4 Oct 16, 2022
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

PySlowFast PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficie

Meta Research 5.3k Jan 03, 2023
An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics.

Sketch Simulator An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics. See

12 Dec 18, 2022
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Egqawkq 12 Jan 05, 2023
PyTorch code for: Learning to Generate Grounded Visual Captions without Localization Supervision

Learning to Generate Grounded Visual Captions without Localization Supervision This is the PyTorch implementation of our paper: Learning to Generate G

Chih-Yao Ma 41 Nov 17, 2022
[2021][ICCV][FSNet] Full-Duplex Strategy for Video Object Segmentation

Full-Duplex Strategy for Video Object Segmentation (ICCV, 2021) Authors: Ge-Peng Ji, Keren Fu, Zhe Wu, Deng-Ping Fan*, Jianbing Shen, & Ling Shao This

Daniel-Ji 55 Dec 22, 2022
Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022)

Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022) Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, and Guang Chen. Uns

Intelligent Vision for Robotics in Complex Environment 91 Dec 30, 2022
🚩🚩🚩

My CTF Challenges 2021 AIS3 Pre-exam / MyFirstCTF Name Category Keywords Difficulty β’Έβ“„β“‹β’Ύβ’Ή-①⑨ (MyFirstCTF Only) Reverse Baby β˜… Piano Reverse C#, .NET β˜…

6 Oct 28, 2021
Official Implementation of PCT

Official Implementation of PCT Prerequisites python == 3.8.5 Please make sure you have the following libraries installed: numpy torch=1.4.0 torchvisi

32 Nov 21, 2022
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning The predictive learning of spatiotemporal sequences aims to generate future

THUML: Machine Learning Group @ THSS 243 Dec 26, 2022
The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

LEAR The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction". **The code is in the "master

杨攀 93 Jan 07, 2023
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | ζ–—εœ°δΈ»AI

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning DouZero is a reinforcement learning framework for DouDizhu (ζ–—εœ°δΈ»), t

Kwai Inc. 3.1k Jan 04, 2023
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and t

305 Dec 16, 2022
A clean and scalable template to kickstart your deep learning project πŸš€ ⚑ πŸ”₯

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project πŸš€ ⚑ πŸ”₯ Click on Use this template to initialize new re

Hyunsoo Cho 1 Dec 20, 2021
Adaptive Attention Span for Reinforcement Learning

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

100 Nov 15, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

78 Dec 27, 2022
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project

This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create

M Faber 769 Dec 08, 2022