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Leveraging ChatGPT in the Loop for Enhanced Robustness in Deep Learning Models | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 7, Volume 2, Issue 2, December 2025, Pages 49-53 PDF (485.01 K) | ||
| Document Type: Research paper | ||
| DOI: 10.69513/jncs.v2.i2.a7 | ||
| Authors | ||
| Mohammed Alawad* 1; Baraa Saeed Ali2 | ||
| 1Wayne State University | ||
| 2University of Anbar | ||
| Abstract | ||
| In sensitive environments like healthcare, the robustness of deep learning models is of utmost importance due to the potential life-threatening consequences of false predictions. While adversarial training is a widely-used approach to enhance deep learning model robustness under adversarial attacks, its effectiveness in such environments remains largely unexplored. This paper proposes a framework for generating adversarial examples in the context of supervised clinical document classification. Specifically, the integration of chatGPT in the loop enables the generation of diverse sets of adversarial examples, targeting various aspects of the classification process such as semantic perturbations, word-level substitutions, sentence rearrangements, polarity shifts, and adversarial phrases. The robustness of DL models against these adversarial examples is thoroughly evaluated. Furthermore, a comprehensive study is conducted to investigate the effectiveness of adversarial training as a defense technique in this sensitive environment. Experimental results demonstrate that the proposed adversarial examples significantly reduce the accuracy of the baseline DL model. Moreover, the study reveals that adversarial training can effectively enhance the model's robustness against adversarial examples. This research sheds light on the potential of leveraging adversarial training in sensitive domains and emphasizes the importance of addressing robustness concerns in DL-based healthcare applications. | ||
| Keywords | ||
| adversarial examples; deep learning; chatGPT; document classification | ||
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