Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
Authors: Bin Huang, Jiaqian Yu, Yiwei Chen, Siyang Pan, Qiang Wang, Zhi Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our backdoor attack can significantly degrade the performance of both two-stream Siamese and one-stream Transformer trackers on the poisoned data while gaining comparable performance with the benign trackers on the clean data. |
| Researcher Affiliation | Collaboration | 1 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China 2 Samsung Research China-Beijing, Beijing, China |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Siam RPN++. Our experiments are based on the open-sourced codes 3. OSTrack. Our experiments are based on the open-sourced codes 4. (Footnote 3: https://github.com/STVIR/pysot, Footnote 4: https://github.com/botaoye/OSTrack) |
| Open Datasets | Yes | The Siam RPN++ tracker is trained on COCO [19], Image Net DET [25], Image Net VID [25] and You Tube-Bounding Boxes [24] datasets... The OSTrack tracker is trained on COCO [19], La SOT [7], GOT10k [11] and Tracking Net [22] datasets... |
| Dataset Splits | Yes | The Siam RPN++ tracker is trained on COCO [19], Image Net DET [25], Image Net VID [25] and You Tube-Bounding Boxes [24] datasets... for OSTrack, we choose three datasets for evaluation, i.e. La SOT [7], La SOT extension (La SOText) [8], GOT10k [11] (validation set) for OSTrack... |
| Hardware Specification | Yes | Experiments are conducted on 4 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions optimizers like 'SGD optimizer' and 'Adam W optimizer' but does not provide specific version numbers for software libraries, frameworks, or programming languages (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The Siam RPN++ tracker is trained... for 20 epochs with a batch size of 28. An SGD optimizer with momentum 0.9, weight decay of 5 × 10−4 and an initial learning rate of 0.005 is adopted. A log learning rate scheduler with a final learning rate of 0.0005 is used. There is also a learning rate warm-up strategy for the first 5 epochs. The OSTrack tracker is trained... for 300 epochs with a batch size of 32. An Adam W optimizer with weight decay of 1 × 10−4 and an initial learning rate of 0.0004 is adopted. The learning rate is scaled to 0.1 times when the epochs reach to 240. |