targeted-adversarial-mnist

KO | EN

gitlite — search

#javascript #ai #typescript #llm #go #machine-learning #deep-learning #nodejs #kubernetes #vue #pytorch #security #mcp #open-source

targeted-adversarial-mnist

(★ 11)

Adversarial attack on a CNN trained on MNIST dataset using Targeted I-FGSM and Targeted MI-FGM

targeted-adversarial-mnist Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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.
![equation](https://image.ibb.co/cHaamS/ifgsm.png)

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
![result](https://raw.githubusercontent.com/srk97/targeted-adversarial-mnist/master/image.jpg)

# 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:

![momentum](https://image.ibb.co/egbvMS/velocity_vector.png)

![image_update](https://image.ibb.co/cmdmT7/x_update.png)

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

## Result
![result](https://raw.githubusercontent.com/srk97/targeted-adversarial-mnist/master/image_momentum.jpg)

### TO-DO
- ~~Refactor~~
- One pixel attack with Differential Evolution
- ~~Momentum~~

# References
- [karpathy's blog](http://karpathy.github.io/2015/03/30/breaking-convnets/)
- [One pixel attack](https://arxiv.org/abs/1710.08864)
- [Boosting Adversarial Attacks with Momentum](https://arxiv.org/pdf/1710.06081.pdf)

targeted-adversarial-mnist

targeted-adversarial-mnist Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

targeted-adversarial-mnist

targeted-adversarial-mnist Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README