Adversarial Texts with Gradient Methods

Understand

Adversarial samples for images have been extensively studied in the literature.

• Among many of the attacking methods, gradient-based methods are both effective and easy to compute.
• In this work, we propose a framework to adapt the gradient attacking methods on images to text domain.
• The main difficulties for generating adversarial texts with gradient methods are i) the input space is discrete, which makes it difficult to accumulate small noise directly in the inputs, and ii) the measurement of the quality of the adversarial texts is difficult.