GenFighter: A Generative and Evolutive Defense Strategy Against Textual Adversarial Attacks

Core Concepts

GenFighter enhances adversarial robustness by learning and reasoning on the training classification distribution, transforming anomalous instances into semantically equivalent ones aligned with the distribution, and employing ensemble techniques for a unified and robust response.

Abstract

The paper introduces GenFighter, a novel defense strategy against adversarial attacks in natural language processing (NLP) tasks. GenFighter operates under the assumption that successful adversarial attacks produce instances that lie outside the distribution of the training data, where the victim model lacks explicit training.
The key components of GenFighter are:

A paraphraser module that generates semantically equivalent instances from the input text.
An anomaly detection model (Gaussian Mixture Model) that learns the distribution of the training data and identifies potentially malicious instances deviating from this distribution.
An evolutionary search procedure that transforms the anomalous instances into ones more aligned with the training distribution.
An ensemble method that combines the classifications of the generated semantically equivalent instances to provide a robust and unified response.

The experiments show that GenFighter outperforms state-of-the-art defenses in accuracy under attack and attack success rate metrics across three datasets and against three strong word-substitution attacks (PWWS, TextFooler, and BERT-Attack) targeting RoBERTa and BERT models. GenFighter also requires a high number of queries per attack, making it challenging to compromise in real scenarios. The ablation study demonstrates the importance of each sub-component in achieving the high performance of GenFighter.

Stats

Adversarial attacks often involve manipulations at the character, word, or sentence level, with word-substitution attacks being the most effective.
GenFighter outperforms state-of-the-art defenses by an absolute average of +41.6% in accuracy under attack, +37.0% in attack success rate, and +7.8% in the number of queries required per attack.
GenFighter consistently achieves the highest number of queries required per attack, making it particularly challenging to compromise.

Quotes

"GenFighter identifies potentially malicious instances deviating from the distribution, transforms them into semantically equivalent instances aligned with the training data, and employs ensemble techniques for a unified and robust response."
"Our experiments show that GenFighter outperforms state-of-the-art defenses in accuracy under attack and attack success rate metrics."
"The ablation study shows that our approach integrates transfer learning, a generative/evolutive procedure, and an ensemble method, providing an effective defense against NLP adversarial attacks."

Key Insights Distilled From

GenFighter: A Generative and Evolutive Textual Attack Removal

by Md Athikul I... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11538.pdf

Deeper Inquiries

How can GenFighter be extended to defend against adversarial attacks that are more aligned with the training classification distribution of the victim model?

GenFighter can be extended to defend against adversarial attacks that are more aligned with the training classification distribution of the victim model by incorporating a more sophisticated anomaly detection technique. Instead of relying solely on a Gaussian Mixture Model (GMM), which may have limitations in capturing complex distributions, GenFighter could explore the use of autoencoders for anomaly detection. Autoencoders can learn more intricate patterns in the data and detect outliers that deviate from the learned distribution more effectively. By leveraging the capabilities of autoencoders, GenFighter can better identify adversarial instances that are aligned with the training data distribution.

How can the paraphrasing methodology in GenFighter be further improved to better preserve the semantics of the input text in specialized NLP tasks?

To enhance the paraphrasing methodology in GenFighter for better preservation of semantics in specialized NLP tasks, the model can incorporate domain-specific knowledge and constraints. By fine-tuning the paraphraser module on domain-specific data or incorporating domain-specific language models, GenFighter can generate paraphrases that are more contextually relevant and semantically aligned with the specialized tasks. Additionally, integrating syntactic and semantic constraints during the paraphrasing process can help ensure that the generated text maintains the intended meaning in specialized domains. By customizing the paraphrasing methodology to the specific characteristics of the NLP tasks, GenFighter can improve the quality of the generated paraphrases and enhance its overall defense effectiveness.

What other text embedding models and anomaly detection techniques could be explored to enhance the performance of GenFighter?

To enhance the performance of GenFighter, alternative text embedding models and anomaly detection techniques can be explored. Instead of relying solely on traditional embedding models like BERT or RoBERTa, GenFighter could incorporate contextual embedding models such as XLNet or ALBERT, which may capture more nuanced semantic relationships in the text. These models can provide richer representations of the input text, leading to better anomaly detection and defense against adversarial attacks.
In terms of anomaly detection techniques, GenFighter could explore the use of variational autoencoders (VAEs) or self-organizing maps (SOMs) for learning the distribution of the training data. VAEs can capture the underlying structure of the data distribution and generate more diverse and meaningful representations, while SOMs can effectively cluster data points and identify outliers. By leveraging these advanced anomaly detection techniques, GenFighter can improve its ability to detect and mitigate adversarial attacks that align closely with the training classification distribution.