Deep Text Classification Can be Fooled (1704.08006v2)

Abstract: In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.

• The paper introduces a framework that uses insertion, modification, and removal perturbations to generate adversarial text examples while preserving semantic content.
• It leverages gradient-based analysis in white-box attacks and fuzzing methods in black-box settings to precisely target influential text components.
• Experimental results reveal that both character-level and word-level models can be misled with high confidence, underscoring the need for robust adversarial training.

Adversarial Vulnerabilities in Deep Learning for Text Classification

The paper "Deep Text Classification Can be Fooled" addresses a significant yet often overlooked issue in the field of NLP: the susceptibility of Deep Neural Networks (DNNs) to adversarial attacks. The authors provide a comprehensive framework to craft adversarial text samples that exploit the vulnerabilities of state-of-the-art character-level and word-level DNN-based text classifiers.

Key Contributions

1. Adversarial Attack Strategies: The paper introduces a novel approach to adversarial attacks on text classifiers, employing three primary perturbation strategies: insertion, modification, and removal. These strategies are designed to generate adversarial samples that can manipulate the classifier's predictions without altering the perceived meaning of the text or being detectable by human observers.
2. Gradient-based Text Analysis: In the context of a white-box attack scenario, the authors leverage the gradient of the cost function to identify critical text components influencing classifier behavior. This analysis enables precise perturbations targeting significant phrases and terms within a text sample.
3. Utility-preserving Adversarial Samples: The adversarial samples crafted using the proposed methods maintain the original semantic content, ensuring that human readers can still recognize the intent of the text, such as a spam message or phishing attempt, even when the system is misled into a misclassification.
4. Practical and Theoretical Evaluation: Extensive experiments validate the effectiveness of the adversarial perturbation techniques against two representative DNN models—a character-level model and a word-level model—utilizing well-known datasets like DBpedia, MR, CR, and MPQA. The results consistently show that these models can be fooled into misclassifying texts into any desired class with high confidence levels.

Methodological Insights

• The use of both white-box and black-box attacks allows for a robust analysis of the classifier vulnerabilities. In white-box scenarios, the authors perform backpropagation to calculate input gradients, while in black-box settings, they adopt a fuzzing-like approach to probe the model's weaknesses.
• The integration of natural language watermarking techniques, such as inserting presuppositions or semantically similar constructs, enriches the adversarial strategies beyond simple character-level modifications.
• The paper highlights that even slight modifications based on common typos can substantially influence classifier decisions when they align with calculated cost gradient directions, underlining the sensitivity of NLP models to structured perturbations.

Implications and Future Directions

This paper underscores the critical need for enhanced robustness in NLP models against adversarial attacks. The findings suggest that adversarial training—a strategy wherein models are exposed to adversarial examples during the learning phase—could be vital in developing resilient classifiers. Additionally, the transferability of adversarial samples across different models calls for broader defensive mechanisms that can generalize beyond specific architectures.

The techniques developed in this work could also pave the way for automated adversarial sample generation, which would support deeper research into adversarial training methods and robustness evaluation. Future research may also explore the application of these adversarial techniques to other domains within AI, examining the limits and capabilities of current classification models across various data types beyond text, such as images and speech.

Overall, this paper provides a significant contribution to the literature on adversarial machine learning, specifically focusing on the nuances of text data manipulation and its implications for the security of NLP systems.