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

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