Adversarial attacks in visual object tracking aim to drop tracking performance through injecting imperceptible perturbations to the input of the tracker. Current methods usually superimpose perturbation maps on the input images, and advocate blinding the tracker via occluding the real targets to achieve attack effect. From the perspective of attraction, we alternatively propose a novel idea of attacking the tracker, which advocates using perturbation patches to act as fake targets to attract the tracker's attention. For this purpose, we establish a multi-conditional objective function to generate our ideal patch in an offline iterative manner. For invisibility, we integrate the constraint of patch value into the function for unified optimization. For universality, in addition to adopting large-scale and high-diversity training samples , we also incorporate the video-agnostic condition into this function. To make the patch attractive like a fake target, we elaborately design the non-overlapping area to determine the patch position, and generate matched fake labels to mislead the tracker to track the patch. In online attacking, it only needs to paste the optimized patch onto the video frames, the tracker will be successfully attracted by our patch, achieving attack effect. Extensive experimental results on 8 popular tracking datasets demonstrate that our method can obtain exceptional attack performance in both non-targeted and targeted attack. Additionally, the experiments on transferability illustrate our optimized patches can be directly applied to other trackers with different architectures.

Hashing techniques have recently gained increasing research interest in multimedia studies. Most existing hashing methods only employ single features for hash code learning. Multiview data with each view corresponding to a type of feature generally provides more comprehensive information. How to efficiently integrate multiple views for learning compact hash codes still remains challenging. In this article, we propose a novel unsupervised hashing method, dubbed multiview discrete hashing (MvDH), by effectively exploring multiview data. Specifically, MvDH performs matrix factorization to generate the hash codes as the latent representations shared by multiple views, during which spectral clustering is performed simultaneously. The joint learning of hash codes and cluster labels enables that MvDH can generate more discriminative hash codes, which are optimal for classification. An efficient alternating algorithm is developed to solve the proposed optimization problem with guaranteed convergence and low computational complexity. The binary codes are optimized via the discrete cyclic coordinate descent (DCC) method to reduce the quantization errors. Extensive experimental results on three large-scale benchmark datasets demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of both accuracy and scalability.