ReLU Defense

ReLU defense against adversarial attacks

** Work in Progress ... **

Start with intro.ipynb

The master branch works with **PyTorch 1.0.0 or higher.

Manuscript is avaiable in Arxiv.

License

This project is released under the Apache 2.0 license.

Citation

If you use this code in your research, please cite this project.

@article{reluDefense2020,
  title={Harnessing adversarial examples with a surprisingly simple defense},
  author={Borji, Al},
  journal={arXiv preprint arXiv:2004.13013},
  year={2020}
}

---

How to use this defense?

First, define the srelu function as below:

def srelu(input, slope):
    return slope * F.relu(input)
    
class SReLU(nn.Module):
    def __init__(self):
        super().__init__() 
        
    def forward(self, input, slope):
        return srelu(input, slope)

Second, define your model as (\eg):

class NetTest(nn.Module):
    def __init__(self, slope):
        super(NetTest, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.slope = slope
        
    def forward(self, x):
        x = srelu(F.max_pool2d(self.conv1(x), 2), self.slope)
        x = srelu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2), self.slope)    
        x = x.view(-1, 320)
        x = srelu(self.fc1(x), self.slope)
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x 

Third, create your model as:

model = NetTest(sl).to(device)

Choose sl > 1.

Contact

This repo is currently maintained by Ali Borji (aliborji@gmail.com).