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..
Pre-trained Adversarial Perturbations
Authors: Yuanhao Ban, Yinpeng Dong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on typical pre-trained vision models and ten downstream tasks demonstrate that our method improves the attack success rate by a large margin compared with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science & Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University 2 Department of Electronic Engineering, Tsinghua University 3 Real AI |
| Pseudocode | No | The paper describes the methods and formulations but does not include structured pseudocode or algorithm blocks that are clearly labeled as such. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/banyuanhao/PAP. |
| Open Datasets | Yes | We adopt the ILSVRC 2012 dataset [42] to generate PAPs, which are also used to pre-train the models. |
| Dataset Splits | No | The paper mentions using a "testing dataset" and lists various datasets like CIFAR10, CIFAR100, etc., which have standard splits, but it does not explicitly provide specific percentages or counts for a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It mentions general terms like "computational resources" but no specifics. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., specific Python, PyTorch, or CUDA versions) needed to replicate the experiment. |
| Experiment Setup | Yes | Unless otherwise specified, we choose a batch size of 16 and a step size of 0.0002. All the perturbations should be within the bound of 0.05 under the ℓ norm. We evaluate the perturbations at the iterations of 1, 000, 5, 000, 30, 000, and 60, 000, and report the best performance. |