Question: How do authors evaluate their models?
Evidence: Adversarial examples are generally minimal perturbations applied to the input data in an effort to expose the regions of the input space where a trained model performs poorly. Prior works BIBREF0, BIBREF1 have demonstrated the ability of an adversary to evade state-of-the-art classifiers by carefully crafting attack examples which can be even imperceptible to humans. Following such approaches, there has been a number of techniques aimed at generating adversarial examples BIBREF2, BIBREF3. Depending on the degree of access to the target model, an adversary may operate in one of the two different settings: (a) black-box setting, where an adversary doesn't have access to target model's internal architecture or its parameters, (b) white-box setting, where an adversary has access to the target model, its parameters, and input feature representations. In both these settings, the adversary cannot alter the training data or the target model itself. Depending on the purpose of the adversary, adversarial attacks can be categorized as (a) targeted attack and (b) non-targeted attack. In a targeted attack, the output category of a generated example is intentionally controlled to a specific target category with limited change in semantic information. While a non-targeted attack doesn't care about the category of misclassified results.
Most of the prior work has focused on image classification models where adversarial examples are obtained by introducing imperceptible changes to pixel values through optimization techniques BIBREF4, BIBREF5. However, generating natural language adversarial examples can be challenging mainly due to the discrete nature of text samples. Continuous data like image or speech is much more tolerant to perturbations compared to text BIBREF6. In textual domain, even a small perturbation is clearly perceptible and can completely change the semantics of the text. Another challenge for generating adversarial examples relates to identifying salient areas of the text where a perturbation can be applied successfully to fool the target classifier. In addition to fooling the target classifier, the adversary is designed with different constraints depending on the task and its motivations BIBREF7. In our work, we focus on constraining our adversary to craft examples with semantic preservation and minimum perturbations to the input text.
Given different settings of the adversary, there are other works that have designed attacks in “gray-box” settings BIBREF8, BIBREF9, BIBREF10. However, the definitions of “gray-box” attacks are quite different in each of these approaches. In this paper, we focus on “black-box” setting where we assume that the adversary possesses a limited set of labeled data, which is different from the target's training data, and also has an oracle access to the system, i.e., one can query the target classifier with any input and get its corresponding predictions. We propose an effective technique to generate adversarial examples in a black-box setting. We develop an Adversarial Example Generator (AEG) model that uses a reinforcement learning framing to generate adversarial examples. We evaluate our models using a word-based BIBREF11 and character-based BIBREF12 text classification model on benchmark classification tasks: sentiment classification and news categorization. The adversarial sequences generated are able to effectively fool the classifiers without changing the semantics of the text. Our contributions are as follows:
We propose a black-box non-targeted attack strategy by combining ideas of substitute network and adversarial example generation. We formulate it as a reinforcement learning task.
We introduce an encoder-decoder that operates over words and characters of an input text and empowers the model to introduce word and character-level perturbations.
We adopt a self-critical sequence training technique to train our model to generate examples that can fool or increase the probability of misclassification in text classifiers.
We evaluate our models on two different datasets associated with two different tasks: IMDB sentiment classification and AG's news categorization task. We run ablation studies on various components of the model and provide insights into decisions of our model.
We evaluate our models using a word-based and character-based text classification model on benchmark classification tasks: sentiment classification and news categorization.
Answer: Using a word-based and character-based text classification model on benchmark classification tasks: sentiment classification and news categorization.