Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where small perturbations in the input could significantly impact the outcome of the system. Common countermeasures include leveraging adversarial training and/or data or network transformation. Although widely used, the main drawback of these countermeasures is that they require full and invasive access to the classifiers, which are typically proprietary. Additionally, the cost of training or retraining is often prohibitively expensive for large models. To tackle this, purification models have recently been proposed. The aim is to incorporate a "purification" layer before classification, thereby eliminating the necessity to modify the classifier. Despite their effectiveness, state-of-the-art purification methods are compute-intensive, rendering them unsuitable for mobile systems where resources are constrained and large latency is not desired.