Multi-Model UNet: An Adversarial Defense Mechanism for Robust Visual Tracking

Currently, state-of-the-art object-tracking algorithms are facing a severe threat from adversarial attacks, which can significantly undermine their performance. In this research, we introduce MUNet, a novel defensive model designed for visual tracking. This model is capable of generating defensive images that can effectively counter attacks while maintaining a low computational overhead. To achieve this, we experiment with various configurations of MUNet models, finding that even a minimal three-layer setup significantly improves tracking robustness when the target tracker is under attack. Each model undergoes end-to-end training on randomly paired images, which include both clean and adversarial noise images. This training separately utilizes pixel-wise denoiser and feature-wise defender. Our proposed models significantly enhance tracking performance even when the target tracker is attacked or the target frame is clean. Additionally, MUNet can simultaneously share its parameters on both template and search regions. In experimental results, the proposed models successfully defend against top attackers on six benchmark datasets, including OTB100, LaSOT, UAV123, VOT2018, VOT2019, and GOT-10k. Performance results on all datasets show a significant improvement over all attackers, with a decline of less than 4.6% for every benchmark metric compared to the original tracker. Notably, our model demonstrates the ability to enhance tracking robustness in other blackbox trackers.