Title: Automotive Object Detection under Adversarial Attacks: A Robust Two-Stage Training Framework
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: adversarial robustness, object detection, Faster R-CNN, adversarial examples, automotive perception, binary classifier, two-phase training, FGSM, PGD, BIM, adversarial detection, deep learning, model robustness, adversarial defense, transfer learning

Abstract: Deep learning-based object detectors are widely used in autonomous driving, but remain vulnerable to adversarial attacks. In this paper, we propose a lightweight and modular two-stage training framework that enhances adversarial robustness by integrating a binary adversarial classifier into the backbone of a Faster Region-Based Convolutional Neural Network (Faster R-CNN). The classifier is trained in two phases: (1) freezing the detector while learning to distinguish adversarial inputs, and (2) joint fine-tuning to refine performance. Our method achieves over 90\% accuracy in detecting adversarial examples and generalizes well across white-box attack types and strengths, including the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Projected Gradient Descent (PGD). The model demonstrated strong zero-shot transferability and achieved further gains after fine-tuning on the target datasets. We evaluated the approach on the Berkeley DeepDrive 100K (BDD100K) dataset and further demonstrated strong cross-dataset robustness on KITTI and nuScenes: the model transfers effectively in a zero-shot setting and achieves AUROC above 0.98 under BIM and PGD attacks after fine-tuning. The proposed defense improves adversarial perturbation detection with minimal impact on object detection accuracy, making it suitable for real-world deployment in automotive systems.

TL;DR: We propose a practical two-stage training method that boosts automotive object detector robustness against adversarial attacks with a lightweight, accurate binary classifier.