Martinović, IvanMateo Sanguino, Tomás JesúsJovanović, JovanaJovanovic, MihailoDjukanović, Milena2025-06-162025-06-162025-06Martinović, I., Mateo Sanguino, T. d. J., Jovanović, J., Jovanović, M., & Djukanović, M. (2025). One Possible Path Towards a More Robust Task of Traffic Sign Classification in Autonomous Vehicles Using Autoencoders. Electronics, 14(12), 2382. https://doi.org/10.3390/electronics141223822079-9292 (electrónico)https://hdl.handle.net/10272/25713The increasing deployment of autonomous vehicles (AVs) has exposed critical vulnerabilities in traffic sign classification systems, particularly against adversarial attacks that can compromise safety. This study proposes a dual-purpose defense framework based on convolutional autoencoders to enhance robustness against two prominent whitebox attacks: Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Experiments on the German Traffic Sign Recognition Benchmark (GTSRB) dataset show that, although these attacks can significantly degrade system performance, the proposed models are capable of partially recovering lost accuracy. Notably, the defense demonstrates strong capabilities in both detecting and reconstructing manipulated traffic signs, even under low-perturbation scenarios. Additionally, a feature-based autoencoder is introduced, which—despite a high false positive rate—achieves perfect detection in critical conditions, a tradeoff considered acceptable in safety-critical contexts. These results highlight the potential of autoencoder-based architectures as a foundation for resilient AV perception while underscoring the need for hybrid models integrating visual-language frameworks for real-time, fail-safe operation.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Traffic signClassification taskAdversarial attackFGSMPGDAutoencoderjournal article10.3390/electronics14122382open access3327 Tecnología de Los Sistemas de Transporte3317 Tecnología de Vehículos de Motor