Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization

• URL: http://arxiv.org/abs/2509.23961v1
• Date: Sun, 28 Sep 2025 16:31:30 GMT
• Title: Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
• Authors: Sheikh Md Mushfiqur Rahman, Nasir Eisty,
• Abstract summary: This project aims to enhance fault detection and model robustness in Deep Neural Networks (DNNs)<n>Our method selects a subset of adversarial inputs with a high likelihood of exposing model faults without relying on architecture-specific characteristics or formal verification.<n>By efficiently organizing test permutations, it uncovers all potential faults significantly faster across various datasets, model architectures, and adversarial attack techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Deep Neural Networks (DNNs) are increasingly deployed in critical applications, where resilience against adversarial inputs is paramount. However, whether coverage-based or confidence-based, existing test prioritization methods often fail to efficiently identify the most fault-revealing inputs, limiting their practical effectiveness. Aims: This project aims to enhance fault detection and model robustness in DNNs by integrating Learning-Based Testing (LBT) with hypothesis and mutation testing to efficiently prioritize adversarial test cases. Methods: Our method selects a subset of adversarial inputs with a high likelihood of exposing model faults, without relying on architecture-specific characteristics or formal verification, making it adaptable across diverse DNNs. Results: Our results demonstrate that the proposed LBT method consistently surpasses baseline approaches in prioritizing fault-revealing inputs and accelerating fault detection. By efficiently organizing test permutations, it uncovers all potential faults significantly faster across various datasets, model architectures, and adversarial attack techniques. Conclusion: Beyond improving fault detection, our method preserves input diversity and provides effective guidance for model retraining, further enhancing robustness. These advantages establish our approach as a powerful and practical solution for adversarial test prioritization in real-world DNN applications.

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