README.md

targeted-adversarial-mnist

Adversarial attack on a CNN trained on MNIST dataset using Targeted Iterative Fast Gradient Sign Method and Targeted Momentum Iterative Fast Gradient Method

Dependencies

• Tensorflow
• numpy

The model.py file defines the architecture and saves the trained model.

Architecture

• Convolutional layer 1: 32 5x5x1 kernels
• Relu activation
• Standard Max Pooling
• Convolutional layer 2: 64 5x5x32 kernels
• Relu activation
• Standard Max Pooling
• Fully Connected Layer 1 with 1024 out units
• Relu activation
• Dropout
• Fully Connected Layer 2 with 10 out units (representing 10 classes of the dataset)

Targeted I-FGSM

The adversary.py script creates the adversarial examples. It takes 2 arguments

• --input_class or -i
• --target_class or -t

Input class is the actual label of the input image.

Target class is the label that we want the network to predict for the input image

The image is modified by taking the gradient of the cost function w.r.t the input.

The pre-trained model is present in the model folder. So, the adversary script can be run directly.

python adversary.py -i 2 -t 6

The default parameters are: EPSILON=0.01 and SAMPLE_SIZE=10.

Result

Targeted MI-FGM

The adversary_momentum.py script creates adversarial examples using the momentum update. It takes the same arguments as the adversary.py script The update equations are:

The default parameters are: MU=1,EPSILON=0.01 and SAMPLE_SIZE=10.

Result

TO-DO

• Refactor
• One pixel attack with Differential Evolution
• Momentum

References

• karpathy's blog
• One pixel attack
• Boosting Adversarial Attacks with Momentum