Defense Methods Against Adversarial Examples for Recurrent Neural Networks

Understand

Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state-of-the-art performance.

• Until recently, research on both attack and defense methods focused on image recognition, primarily using convolutional neural networks (CNNs).
• In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are also vulnerable to such attacks.
• In this paper, we present a novel defense method, termed sequence squeezing, to make RNN classifiers more robust against such attacks.