On Adversarial Examples for Text Classification by Perturbing Latent Representations
This work addresses robustness issues in text classification for AI security, but it is incremental as it adapts existing white-box attack methods to a new domain.
The paper tackles the vulnerability of deep learning text classifiers to adversarial examples by proposing a framework that perturbs latent representations instead of discrete inputs, enabling white-box attacks to measure classifier robustness.
Recently, with the advancement of deep learning, several applications in text classification have advanced significantly. However, this improvement comes with a cost because deep learning is vulnerable to adversarial examples. This weakness indicates that deep learning is not very robust. Fortunately, the input of a text classifier is discrete. Hence, it can prevent the classifier from state-of-the-art attacks. Nonetheless, previous works have generated black-box attacks that successfully manipulate the discrete values of the input to find adversarial examples. Therefore, instead of changing the discrete values, we transform the input into its embedding vector containing real values to perform the state-of-the-art white-box attacks. Then, we convert the perturbed embedding vector back into a text and name it an adversarial example. In summary, we create a framework that measures the robustness of a text classifier by using the gradients of the classifier.