Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Understand

Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs).

• However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite.
• In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities.
• To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization.