DATASET (CIFAR-10): https://www.cs.toronto.edu/~kriz/cifar.html
Implement your own convolutional network, deciding how many layers, the type of layers and how they are interleaved, the type of pooling, the use of residual connections, etc. Discuss why you made each choice a provide performance results of your CNN on CIFAR-10.
Now that your network is trained, you might try an adversarial attack to it. Try the simple Fast Gradient Sign method, generating one (or more) adversarial examples starting from one (or more) CIFAR-10 test images. It is up to you to decide if you want to implement the attack on your own or use one of the available libraries (e.g. foolbox, CleverHans, ...). Display the original image, the adversarial noise and the final adversarial example.