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.