Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models

by   Pranath Reddy Kumbam, et al.

The advent of social media has given rise to numerous ethical challenges, with hate speech among the most significant concerns. Researchers are attempting to tackle this problem by leveraging hate-speech detection and employing language models to automatically moderate content and promote civil discourse. Unfortunately, recent studies have revealed that hate-speech detection systems can be misled by adversarial attacks, raising concerns about their resilience. While previous research has separately addressed the robustness of these models under adversarial attacks and their interpretability, there has been no comprehensive study exploring their intersection. The novelty of our work lies in combining these two critical aspects, leveraging interpretability to identify potential vulnerabilities and enabling the design of targeted adversarial attacks. We present a comprehensive and comparative analysis of adversarial robustness exhibited by various hate-speech detection models. Our study evaluates the resilience of these models against adversarial attacks using explainability techniques. To gain insights into the models' decision-making processes, we employ the Local Interpretable Model-agnostic Explanations (LIME) framework. Based on the explainability results obtained by LIME, we devise and execute targeted attacks on the text by leveraging the TextAttack tool. Our findings enhance the understanding of the vulnerabilities and strengths exhibited by state-of-the-art hate-speech detection models. This work underscores the importance of incorporating explainability in the development and evaluation of such models to enhance their resilience against adversarial attacks. Ultimately, this work paves the way for creating more robust and reliable hate-speech detection systems, fostering safer online environments and promoting ethical discourse on social media platforms.


page 1

page 2

page 3

page 4


Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

The growing use of social media has led to the development of several Ma...

Transferability of Adversarial Attacks on Synthetic Speech Detection

Synthetic speech detection is one of the most important research problem...

On The Robustness of Offensive Language Classifiers

Social media platforms are deploying machine learning based offensive la...

Exploring the Physical World Adversarial Robustness of Vehicle Detection

Adversarial attacks can compromise the robustness of real-world detectio...

RECAST: Interactive Auditing of Automatic Toxicity Detection Models

As toxic language becomes nearly pervasive online, there has been increa...

SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models

Skeleton-based human action recognition technologies are increasingly us...

Can Rationalization Improve Robustness?

A growing line of work has investigated the development of neural NLP mo...

Please sign up or login with your details

Forgot password? Click here to reset