Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Overview

Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run the following commands depending on the experiment:

For the MNIST experiment:
python ./mnist/clm_train.py --folder 
   
     --nmodel 
    
      --alpha 
     
       --delta 
      
        --pre 
       
         --pref 
        
          --epochs 
         
           --prse 
          
            --lr 
           
             --adv 
             For the CIFAR-10 experiment: python ./cifar-10/clm_train.py --folder 
             
               --nmodel 
              
                --alpha 
               
                 --delta 
                
                  --pre 
                 
                   --pref 
                  
                    --epochs 
                   
                     --prse 
                    
                      --lr 
                     
                       --adv 
                     
                    
                   
                  
                 
                
               
              
             
             
           
          
         
        
       
      
     
    
   

Evaluation

To evaluate the models against adversarial attacks, run the following commands depending on the experiment:

For the MNIST experiment:
python ./mnist/mra.py --attack 
   
     --folder 
    
      --nmodel 
     
       --epsilon 
      
        --testid 
       
         --batch 
        
          For the CIFAR-10 experiment: python ./cifar-10/attack.py --attack 
         
           --folder 
          
            --nmodel 
           
             --epsilon 
            
              --testid 
             
               --batch 
              
                The following is the list of attacks you can test against: - fgsm: Fast Gradient Sign Method attack - pgd: Projected Gradient Descent attack - Linf - auto: AutoAttack - mifgsm: MI-FGSM attack. 
              
             
            
           
          
         
        
       
      
     
    
   

Pre-trained Models

Pretrained models are included in the folders of mnist and cifar-10.

Since GitHub has a limit of the size of uploaded files, you can download the pretrained models through this link: https://drive.google.com/drive/folders/1Dkupi4bObIKofjKZOwOG0owsBFwfwo_5?usp=sharing

├── LICENSE
├── README.md
├── __init__.py
├── cifar-10
│   ├── clm10-a0.5d0.1-epochs150-prse10 
   
    
│   ├── clm_adv4-a0.1d0.05-epochs150-prse10 
    
     
│   ├── clm_train.py
│   ├── mra.py
│   ├── ulm10 
     
      
│   └── ulm_adv4 
      
       
├── mnist
│   ├── clm10-a0.1d0.1-epochs5-prse10 
       
         │   ├── clm_adv4-a0.01d0.005-epochs5-prse1 
        
          │   ├── clm_train.py │   ├── mra.py │   ├── ulm10 
         
           │   └── ulm_adv4 
          
            ├── models │   ├── lenet5.py │   └── resnet.py └── requirements.txt 
          
         
        
       
      
     
    
   

Contributing

MIT License

Deep Latent Force Models

Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona

Tom McDonald 5 Oct 26, 2022
Image process framework based on plugin like imagej, it is esay to glue with scipy.ndimage, scikit-image, opencv, simpleitk, mayavi...and any libraries based on numpy

Introduction ImagePy is an open source image processing framework written in Python. Its UI interface, image data structure and table data structure a

ImagePy 1.2k Dec 29, 2022
PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time

PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time The implementation is based on SIGGRAPH Aisa'20. Dependencies Python 3.7 Ubuntu

soratobtai 124 Dec 08, 2022
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

40 Dec 13, 2022
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
Semantic similarity computation with different state-of-the-art metrics

Semantic similarity computation with different state-of-the-art metrics Description • Installation • Usage • License Description TaxoSS is a semantic

6 Jun 22, 2022
simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

Ramón Casero 1 Jan 07, 2022
SPRING is a seq2seq model for Text-to-AMR and AMR-to-Text (AAAI2021).

SPRING This is the repo for SPRING (Symmetric ParsIng aNd Generation), a novel approach to semantic parsing and generation, presented at AAAI 2021. Wi

Sapienza NLP group 98 Dec 21, 2022
SWA Object Detection

SWA Object Detection This project hosts the scripts for training SWA object detectors, as presented in our paper: @article{zhang2020swa, title={SWA

237 Nov 28, 2022
It is a simple library to speed up CLIP inference up to 3x (K80 GPU)

CLIP-ONNX It is a simple library to speed up CLIP inference up to 3x (K80 GPU) Usage Install clip-onnx module and requirements first. Use this trick !

Gerasimov Maxim 93 Dec 20, 2022
Do Neural Networks for Segmentation Understand Insideness?

This is part of the code to reproduce the results of the paper Do Neural Networks for Segmentation Understand Insideness? [pdf] by K. Villalobos (*),

biolins 0 Mar 20, 2021
Code for the Lovász-Softmax loss (CVPR 2018)

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Maxim Berman 1.3k Jan 04, 2023
Library to enable Bayesian active learning in your research or labeling work.

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
[ICCV 2021] Learning A Single Network for Scale-Arbitrary Super-Resolution

ArbSR Pytorch implementation of "Learning A Single Network for Scale-Arbitrary Super-Resolution", ICCV 2021 [Project] [arXiv] Highlights A plug-in mod

Longguang Wang 229 Dec 30, 2022
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
Adversarial Self-Defense for Cycle-Consistent GANs

Adversarial Self-Defense for Cycle-Consistent GANs This is the official implementation of the CycleGAN robust to self-adversarial attacks used in pape

Dina Bashkirova 10 Oct 10, 2022
A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

A Pytorch Implementation of Source data‐free domain adaptation of object detector through domain‐specific perturbation Please follow Faster R-CNN and

1 Dec 25, 2021
Implementation of BI-RADS-BERT & The Advantages of Section Tokenization.

BI-RADS BERT Implementation of BI-RADS-BERT & The Advantages of Section Tokenization. This implementation could be used on other radiology in house co

1 May 17, 2022
A tensorflow model that predicts if the image is of a cat or of a dog.

Quick intro Hello and thank you for your interest in my project! This is the backend part of a two-repo application. The other part can be found here

Tudor Matei 0 Mar 08, 2022
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

12 Dec 26, 2022