Robust attacks on Neural Networks

Neural networks are highly vulnerable to evasion attacks. This project contains code to perform these attacks in a robust manner to evaluate future possible defenses.

Break of Defensive Distillation

Defensive Distillation was recently proposed as a defense to adversarial examples. This project contains the TensorFlow models required to train a defensively distilled network and show it is broken.

Brainfuck interpreter in printf

Printf is, unintentionally, a Turing-complete language. We demonstrate this by implementing a brainfuck interpreter through using only calls to the standard C printf.