Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises
arXiv:2003.09595
Abstract
Adversarial attack of CNN aims at deceiving models to misbehave by adding imperceptible perturbations to images. This feature facilitates to understand neural networks deeply and to improve the robustness of deep learning models. Although several works have focused on attacking image classifiers and object detectors, an effective and efficient method for attacking single object trackers of any target in a model-free way remains lacking. In this paper, a cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers. An effective and efficient perturbation generator is trained with a carefully designed adversarial loss, which can simultaneously cool hot regions where the target exists on the heatmaps and force the predicted bounding box to shrink, making the tracked target invisible to trackers. Numerous experiments on OTB100, VOT2018, and LaSOT datasets show that our method can effectively fool the state-of-the-art SiameseRPN++ tracker by adding small perturbations to the template or the search regions. Besides, our method has good transferability and is able to deceive other top-performance trackers such as DaSiamRPN, DaSiamRPN-UpdateNet, and DiMP. The source codes are available at https://github.com/MasterBin-IIAU/CSA.
CVPR2020
References in corpus (7)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
- Synthesizing Robust Adversarial Examples
- Generating Adversarial Examples with Adversarial Networks
- NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
- Foveation-based Mechanisms Alleviate Adversarial Examples
- Transferable Adversarial Attacks for Image and Video Object Detection