Adversarial Reprogramming of Text Classification Neural Networks

Understand

Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network.

• An adversary in such an attack scenario trains an additive contribution to the inputs to repurpose the neural network for the new classification task.
• While this reprogramming approach works for neural networks with a continuous input space such as that of images, it is not directly applicable to neural networks trained for tasks such as text classification, where the input space is discrete.
• Repurposing such classification networks would require the attacker to learn an adversarial program that maps inputs from one discrete space to the other.