  Transformer-based text classifiers like BERT, Roberta, T5, and GPT-3 have
shown impressive performance in NLP. However, their vulnerability to
adversarial examples poses a security risk. Existing defense methods lack
interpretability, making it hard to understand adversarial classifications and
identify model vulnerabilities. To address this, we propose the
Interpretability and Transparency-Driven Detection and Transformation (IT-DT)
framework. It focuses on interpretability and transparency in detecting and
transforming textual adversarial examples. IT-DT utilizes techniques like
attention maps, integrated gradients, and model feedback for interpretability
during detection. This helps identify salient features and perturbed words
contributing to adversarial classifications. In the transformation phase, IT-DT
uses pre-trained embeddings and model feedback to generate optimal replacements
for perturbed words. By finding suitable substitutions, we aim to convert
adversarial examples into non-adversarial counterparts that align with the
model's intended behavior while preserving the text's meaning. Transparency is
emphasized through human expert involvement. Experts review and provide
feedback on detection and transformation results, enhancing decision-making,
especially in complex scenarios. The framework generates insights and threat
intelligence empowering analysts to identify vulnerabilities and improve model
robustness. Comprehensive experiments demonstrate the effectiveness of IT-DT in
detecting and transforming adversarial examples. The approach enhances
interpretability, provides transparency, and enables accurate identification
and successful transformation of adversarial inputs. By combining technical
analysis and human expertise, IT-DT significantly improves the resilience and
trustworthiness of transformer-based text classifiers against adversarial
attacks.
