Towards Universal Physical Attacks on Single Object Tracking
Authors: Li Ding, Yongwei Wang, Kaiwen Yuan, Minyang Jiang, Ping Wang, Hua Huang, Z. Jane Wang1236-1245
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the effectiveness of the physically feasible attacks on Siam Mask and Siam RPN++ visual trackers both in digital and physical scenes. In this section, we empirically evaluate the effectiveness of the proposed attacks on visual tracking both in digital and physically feasible scenes. |
| Researcher Affiliation | Academia | Li Ding1,2*, Yongwei Wang2*, Kaiwen Yuan2, Minyang Jiang2, Ping Wang1 , Hua Huang3 , Z. Jane Wang2 1School of Information and Communications Engineering, Xi an Jiaotong University, 2Department of Electrical and Computer Engineering, University of British Columbia, 3School of Artificial Intelligence, Beijing Normal University {dinglijay,yongweiw,kaiwen, minyang, zjanew}@ece.ubc.ca, ping.fu@xjtu.edu.cn, huahuang@bnu.edu.cn |
| Pseudocode | Yes | Algorithm 1: The proposed algorithm of universal and physically feasible attacks on visual tracking. |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing the code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | For the physically feasible attacks in digital scenes, we experimented on three object categories: person, car and cup from the Large-scale Single Object Tracking (La SOT) dataset (Fan et al. 2019). |
| Dataset Splits | No | The paper states it randomly selects one video for adversarial patch generation and attacks the rest 19 videos, implicitly defining a test set, but it does not specify a separate validation split or its percentage/counts. |
| Hardware Specification | Yes | The experiments were conducted on one NVIDIA RTX-2080 Ti GPU card using Py Torch (Paszke et al. 2019). |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | In all experiments, we keep the patch and object size ratio within 20% to be physically feasible. For parameters in the overall loss expression in Eq.(6), we set D = 3, and the loss weights are set respectively as: α = 1000, β = 1, γ = 0.1. In the Shape loss in Eq.(4), we set K = 20. More concretely, for the shrinking attack, we set h = 1, w = 1, mτ = 0.7; and for the dilation attack, we use h = 1, w = 1, mτ = 0.7. We employ the Adam optimizer from the Py Torch platform with hyperparameters: exponential decays β1 = 0.9, β2 = 0.999, learning rate lr = 10 (for intensity between [0,255]), weight decay set as 0, the batchsize set as 20, and the maximum training epochs M = 300. |