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..
Adversarial Bone Length Attack on Action Recognition
Authors: Nariki Tanaka, Hiroshi Kera, Kazuhiko Kawamoto2335-2343
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on the NTU RGB+D and HDM05 datasets and demonstrate that the proposed attack successfully deceived models with sometimes greater than 90% success rate by small perturbations. |
| Researcher Affiliation | Academia | Nariki Tanaka,1 Hiroshi Kera,2 Kazuhiko Kawamoto,2 1 Graduate School of Science and Engineering, Chiba University 2 Graduate School of Engineering, Chiba University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Pseudocode of adversarial bone length attack |
| Open Source Code | No | The paper provides links to the official code for the *target models* (ST-GCN and SGN) but not to the authors' own implementation of the proposed adversarial bone length attack methodology. |
| Open Datasets | Yes | We used the NTU RGB+D (Shahroudy et al. 2016) and HDM05 (M uller et al. 2007) datasets, which are 3D skeleton action datasets. |
| Dataset Splits | Yes | We randomly divided samples of each class into a training set (80%), validation set (10%), and testing set (10%). |
| Hardware Specification | Yes | All experiments were conducted using an Intel Core i7-6850K CPU and TITAN RTX GPU. |
| Software Dependencies | No | The paper mentions using Python and related libraries for deep learning (e.g., implied by ST-GCN, SGN, PyTorch/TensorFlow frameworks), but does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | The maximum number of iterations of the PGD was set to 50. The step size was set to α = 0.01, as in (Liu, Akhtar, and Mian 2020). |