Leveraging Image-Based Transformations to Mitigate Adversarial Attacks in AI-Based Safety-Critical Systems
Martí Caro, Axel Brando, Jaume Abella · 2025
Dual (DMR) and Triple Modular Redundancy (TMR) are widely used techniques to provide fault detection and/or tolerance capabilities in safety-critical systems through – often diverse – redundancy. However, these systems remain vulnerable to adversarial attacks, which can mislead the AI models and lead to severe consequences. In this paper, we propose enhanced DMR and TMR implementations for image-based object detection leveraging image transformations during inference to mitigate the impact of adversarial attacks, hence addressing safety and security concerns simultaneously. Our approach achieves up to 12.9% and 12.2% higher accuracy in adversarial scenarios compared to state-of-the-art solutions in DMR and TMR configurations, respectively.