Learning Universal Adversarial Perturbation by Adversarial Example
Authors: Maosen Li, Yanhua Yang, Kun Wei, Xu Yang, Heng Huang1350-1358
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |