DHR-OD: Dynamic Heterogeneous Redundancy for Robust Object Detection

Object detection underpins safety-critical applications such as autonomous driving and intelligent surveillance, yet the inherent vulnerability of object detection models exposes them to the dual threat of adversarial and backdoor attacks. Existing defense methods are mostly passive, relying on prior knowledge of known attacks and thus failing to defend against unknown attacks. To address these problems, we introduce Dynamic Heterogeneous Redundancy (DHR) into object detection and build the Dynamic Heterogeneous Redundancy for robust Object Detection (DHR-OD) framework. DHR-OD defends against both threats through four modules, namely a heterogeneous executor pool, dynamic scheduling, multi-model arbitration, and closed-loop feedback, of which the multi-model arbitration module is the core. It fuses the box-level outputs of multiple heterogeneous detectors and identifies anomalies, turning the output differences among heterogeneous models into a defense. Experiments on COCO and PASCAL VOC show that DHR-OD retains 88.9% accuracy under PGD-20 attack and reduces the BadDet backdoor attack success rate from about 100% to about 35% without a dedicated detection module, making it a defense framework that covers both adversarial and backdoor threats while not relying on prior knowledge of specific attack forms.