Adversarial Reprogramming of Neural Networks

Understand

Deep neural networks are susceptible to \emph{adversarial} attacks.

• In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer.
• Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker.
• We introduce attacks that instead {\em reprogram} the target model to perform a task chosen by the attacker---without the attacker needing to specify or compute the desired output for each test-time input.