  Recently, the majority of visual trackers adopt Convolutional Neural Network
(CNN) as their backbone to achieve high tracking accuracy. However, less
attention has been paid to the potential adversarial threats brought by CNN,
including Siamese network.
  In this paper, we first analyze the existing vulnerabilities in Siamese
trackers and propose the requirements for a successful adversarial attack. On
this basis, we formulate the adversarial generation problem and propose an
end-to-end pipeline to generate a perturbed texture map for the 3D object that
causes the trackers to fail. Finally, we conduct thorough experiments to verify
the effectiveness of our algorithm. Experiment results show that adversarial
examples generated by our algorithm can successfully lower the tracking
accuracy of victim trackers and even make them drift off. To the best of our
knowledge, this is the first work to generate 3D adversarial examples on visual
trackers.
