论文标题
视觉对象跟踪的有效对抗攻击
Efficient Adversarial Attacks for Visual Object Tracking
论文作者
论文摘要
视觉对象跟踪是一项重要的任务，需要跟踪器快速准确地找到对象。现有的最先进的对象跟踪器，即基于暹罗的跟踪器，使用DNN来获得高精度。但是，很少探索视觉跟踪模型的鲁棒性。在本文中，我们根据暹罗网络分析对象跟踪器的弱点，然后将对抗性示例扩展到视觉对象跟踪。我们提出了一个端到端网络风扇（快速攻击网络），该网络使用新颖的漂移损失与嵌入式功能损失相结合来攻击基于暹罗网络的跟踪器。在单个GPU下，风扇在训练速度方面有效，并且具有强大的攻击性能。风扇可以在10毫秒以10毫秒的形式产生对抗性示例，实现有效的靶向攻击（OTB上的降低至少40％）和无靶向的攻击（OTB上的下降率至少70％）。
Visual object tracking is an important task that requires the tracker to find the objects quickly and accurately. The existing state-ofthe-art object trackers, i.e., Siamese based trackers, use DNNs to attain high accuracy. However, the robustness of visual tracking models is seldom explored. In this paper, we analyze the weakness of object trackers based on the Siamese network and then extend adversarial examples to visual object tracking. We present an end-to-end network FAN (Fast Attack Network) that uses a novel drift loss combined with the embedded feature loss to attack the Siamese network based trackers. Under a single GPU, FAN is efficient in the training speed and has a strong attack performance. The FAN can generate an adversarial example at 10ms, achieve effective targeted attack (at least 40% drop rate on OTB) and untargeted attack (at least 70% drop rate on OTB).
