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..
Learning Universal Adversarial Perturbation by Adversarial Example
Authors: Maosen Li, Yanhua Yang, Kun Wei, Xu Yang, Heng Huang1350-1358
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method improves the performance by a significant margin over the existing methods in both data-dependent and data-independent settings. |
| Researcher Affiliation | Academia | Maosen Li1, Yanhua Yang1 , Kun Wei1, Xu Yang1, Heng Huang2 1Xidian University, Xi an 710071, China 2Department of Electrical and Computer Engineering, University of Pittsburgh, PA 15260, USA |
| Pseudocode | Yes | Algorithm 1: Our UAP algorithm |
| Open Source Code | Yes | Code is available at https://github.com/lisenxd/AT-UAP. |
| Open Datasets | Yes | We use the Image Net validation set (Russakovsky et al. 2015) containing 50,000 samples to evaluate the performance. We also explore generating data-dependent UAP with the Image Net training data. |
| Dataset Splits | Yes | We use the Image Net validation set (Russakovsky et al. 2015) containing 50,000 samples to evaluate the performance. |
| Hardware Specification | Yes | All of our experiments are conducted on Pytorch and run with single NVIDIA TITAN Xp GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | The number of iterations T, batch-size m, learning rate γ and trade-off factor λ are set to 1000, 32, 0.5 and 0.05, respectively. ϵ2, constant a0 and zoom factor α are set to 20, 14, 20 respectively for data-independent setting and in data-dependent setting, ϵ2 is set to 4. |