On the Transferability of Adversarial Attacksagainst Neural Text Classifier
This research addresses the vulnerability of neural text classifiers to transferable adversarial attacks, which is a significant security concern for users and developers of these models.
This paper investigates the transferability of adversarial examples across text classification models, exploring how architectural and data-related factors influence this phenomenon. They propose a genetic algorithm to generate adversarial examples that can fool nearly all existing models, and derive word replacement rules for model diagnostics.
Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can fool another model. In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples. Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. Such adversarial examples reflect the defects of the learning process and the data bias in the training set. Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.