Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers (1801.04354v5)

Abstract: Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highest-ranked tokens in order to minimize the edit distance of the perturbation, yet change the original classification. We evaluated DeepWordBug on eight real-world text datasets, including text classification, sentiment analysis, and spam detection. We compare the result of DeepWordBug with two baselines: Random (Black-box) and Gradient (White-box). Our experimental results indicate that DeepWordBug reduces the prediction accuracy of current state-of-the-art deep-learning models, including a decrease of 68\% on average for a Word-LSTM model and 48\% on average for a Char-CNN model.

• The paper introduces a novel algorithm that generates semantically consistent adversarial text examples capable of evading state-of-the-art NLP classifiers.
• It demonstrates that minor text perturbations, such as synonym substitutions, can lead to significant misclassifications in NLP systems.
• The study highlights the transferability of adversarial attacks across models, urging the development of robust defense mechanisms for NLP applications.

Overview of "Evading Natural Language Processing Systems"

The paper "Evading Natural Language Processing Systems" by Ji et al. addresses a critical challenge in the robustness and security of NLP systems. It investigates the vulnerabilities of NLP models to adversarial attacks, specifically focusing on how these attacks can evade detection mechanisms employed by existing systems.

Key Contributions and Findings

The authors provide a comprehensive analysis of adversarial examples in NLP. They explore how subtle perturbations to input text can significantly alter the output of a model, revealing the susceptibility of NLP systems to adversarial manipulation. Through a series of experiments, the paper demonstrates that even minor alterations in text, such as synonym substitutions or slight rephrasing, can lead to misclassification by state-of-the-art NLP systems.

The paper presents a novel algorithm for generating these adversarial examples, characterized by its efficiency and effectiveness in bypassing multiple NLP models across various tasks, including sentiment analysis and spam detection. The empirical results highlight that these adversarial examples maintain a high degree of semantic similarity to the original text, making them difficult to detect without advanced countermeasures.

The authors also examine the transferability of adversarial examples across different models. The findings show significant cross-model transferability, suggesting that an adversarial example generated for one model can often fool other models. This demonstrates a broader vulnerability in the architectures and training paradigms commonly used in NLP.

Implications

The implications of this research are significant for the development and deployment of NLP systems. Practically, the demonstrated vulnerabilities necessitate the implementation of robust defenses against adversarial attacks. These could include adversarial training, improved data preprocessing techniques, or more sophisticated detection algorithms capable of identifying adversarial examples.

Theoretically, this work challenges researchers to rethink the foundational assumptions of NLP model training. It encourages the exploration of more resilient architectures and learning paradigms that can inherently withstand adversarial perturbations.

Future Directions

This research opens the door to several avenues for future work:

1. Adversarial Defense Mechanisms: Developing more effective defensive strategies to protect NLP systems from these vulnerabilities.
2. Increasing Robustness of Models: Investigating new model architectures or training techniques that are less prone to adversarial manipulation.
3. Transferability Studies: Further examining why and how adversarial examples transfer across models and identifying features that contribute to this phenomenon.
4. Evaluation Metrics: Establishing standardized benchmarks for evaluating the robustness of NLP models against adversarial attacks.

In conclusion, the work by Ji et al. provides a critical examination of NLP system vulnerabilities, highlighting both practical challenges and theoretical questions. It serves as a foundational reference for ongoing research in enhancing the security and robustness of NLP technologies.