Two-for-One: Image-based Transformations to Mitigate Adversarial Attacks and Random Faults at Once in Safety-critical AI Systems

Abstract

Dual Modular Redundancy (DMR) and Triple Modular Redundancy (TMR) are commonly used methods for providing fault detection and/or tolerance in safety-critical systems by incorporating redundant – and often diverse – components. However, these systems can still be susceptible to adversarial attacks that may deceive AI models, potentially leading to severe consequences. In this paper, we introduce enhanced DMR and TMR strategies for image-based object detection, leveraging image transformations during inference to help reduce the impact of adversarial inputs, while preserving the inherent advantages of diverse redundancy for safety purposes. Experimental results demonstrate that our approach significantly improves robustness under adversarial conditions, achieving up to 12.9% and 12.2% higher accuracy than state-of-the-art solutions in DMR and TMR configurations, respectively, when attacks are individually crafted for each image. Furthermore, against universal adversarial attacks, our solution achieves even greater accuracy gains, with up to 26.8% and 26.0% higher accuracy in DMR and TMR configurations, respectively.