Seeing Through Threats: (ADEx) Adversarial Detection through Explainability

Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging and security-critical systems. Most existing defense strategies are limited by domain specificity, architectural dependence, or the need for extensive retraining, making them impractical for real-world deployment. In this work, we propose ADEx, the first framework to integrate low-rank image approximation with explainability-driven analysis for the detection of adversarial samples. ADEx works by extracting a low-rank representation of the input image using Singular Value Thresholding (SVT), and identifying important image regions by computing class-specific gradient maps from the final layers of the classifier. These maps are then compared using Rank-Biased Overlap (RBO) to quantify the degree of attention drift induced by adversarial perturbations. ADEx is designed for adversarial detection in image classification systems, where class-specific gradient-based explanations are well defined. The framework operates without retraining or architectural modification and can be applied to a wide range of differentiable classifiers, provided gradient access is available for explanation generation. Extensive experiments across multiple datasets, architectures, and attack types demonstrate consistent performance, robustness to hyperparameter choices, and low sensitivity to calibration size. The method provides an interpretable and lightweight solution suitable for practical deployment.