• Implement robust training

• Implement a sufficient amount of perturbation

• Compare performance of std model and robust model

• Implement gradient descent for reconstructing features

• Convergence: the robust model does not seem to converge well (may need to pretrain the model first)

• Why does horizontal lines hurt accuracy more significantly than vertical lines?

The MNIST datset is available at http://yann.lecun.com/exdb/mnist/.

If you would like to run this script on your computer, go to /dataset folder and uncompress all the dataset files to that folder.

The perturbated image samples can be seen in figure below. The last column is ground truth. The group IDs correspond to the order of images in the figure.

• perturbation.py: creates and manages perturbations
• load_mnist.py: loading data from MNIST idx format (need to correct endianess if the data format has sizes greater than 1 byte)
• train_std_model.py: trains standard model
• train_pretrained_model: trains a pretrain model as initial weights for robust model
• train_robust_model.py: trains the robust model
• test_std_model: tests the performance of std model on adversarial dataset
• test_robust_model: tests the performance of robust model on adversarial dataset

• Ilyas, Andrew, et al. "Adversarial examples are not bugs, they are features." arXiv preprint arXiv:1905.02175 (2019).