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Transformer-based text classifiers like BERT, Roberta, T5, and GPT-3 haveshown impressive performance in NLP. However, their vulnerability toadversarial examples poses a security risk. Existing defense methods lackinterpretability, making it hard to understand adversarial classifications andidentify model vulnerabilities. To address this, we propose theInterpretability and Transparency-Driven Detection and Transformation (IT-DT)framework. It focuses on interpretability and transparency in detecting andtransforming textual adversarial examples. IT-DT utilizes techniques likeattention maps, integrated gradients, and model feedback for interpretabilityduring detection. This helps identify salient features and perturbed wordscontributing to adversarial classifications. In the transformation phase, IT-DTuses pre-trained embeddings and model feedback to generate optimal replacementsfor perturbed words. By finding suitable substitutions, we aim to convertadversarial examples into non-adversarial counterparts that align with themodel's intended behavior while preserving the text's meaning. Transparency isemphasized through human expert involvement. Experts review and providefeedback on detection and transformation results, enhancing decision-making,especially in complex scenarios. The framework generates insights and threatintelligence empowering analysts to identify vulnerabilities and improve modelrobustness. Comprehensive experiments demonstrate the effectiveness of IT-DT indetecting and transforming adversarial examples. The approach enhancesinterpretability, provides transparency, and enables accurate identificationand successful transformation of adversarial inputs. By combining technicalanalysis and human expertise, IT-DT significantly improves the resilience andtrustworthiness of transformer-based text classifiers against adversarialattacks.

Interpretability and Transparency-Driven Detection and Transformation of Textual Adversarial Examples (IT-DT)