  Adversarial attacks against deep learning models represent a major threat to
the security and reliability of natural language processing (NLP) systems. In
this paper, we propose a modification to the BERT-Attack framework, integrating
Projected Gradient Descent (PGD) to enhance its effectiveness and robustness.
The original BERT-Attack, designed for generating adversarial examples against
BERT-based models, suffers from limitations such as a fixed perturbation budget
and a lack of consideration for semantic similarity. The proposed approach in
this work, PGD-BERT-Attack, addresses these limitations by leveraging PGD to
iteratively generate adversarial examples while ensuring both imperceptibility
and semantic similarity to the original input. Extensive experiments are
conducted to evaluate the performance of PGD-BERT-Attack compared to the
original BERT-Attack and other baseline methods. The results demonstrate that
PGD-BERT-Attack achieves higher success rates in causing misclassification
while maintaining low perceptual changes. Furthermore, PGD-BERT-Attack produces
adversarial instances that exhibit greater semantic resemblance to the initial
input, enhancing their applicability in real-world scenarios. Overall, the
proposed modification offers a more effective and robust approach to
adversarial attacks on BERT-based models, thus contributing to the advancement
of defense against attacks on NLP systems.
