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..
Backpropagating Linearly Improves Transferability of Adversarial Examples
Authors: Yiwen Guo, Qizhang Li, Hao Chen
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that this simple yet effective method obviously outperforms current state-of-the-arts in crafting transferable adversarial examples on CIFAR-10 and Image Net, leading to more effective attacks on a variety of DNNs. |
| Researcher Affiliation | Collaboration | Yiwen Guo Byte Dance AI Lab EMAIL Qizhang Li Byte Dance AI Lab EMAIL Hao Chen University of California, Davis EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code at: https://github.com/qizhangli/linbp-attack. |
| Open Datasets | Yes | We focus on untargeted ℓ attacks on deep image classifiers. Different methods are compared on CIFAR-10 [27] and Image Net [41]... |
| Dataset Splits | No | The paper mentions using 5000 test instances for evaluation and refers to test sets, but it does not explicitly provide details about a validation dataset split or its purpose for hyperparameter tuning. |
| Hardware Specification | Yes | All experiments are performed on an NVIDIA V100 GPU with code implemented using Py Torch [39]. |
| Software Dependencies | No | The paper mentions that code was implemented using 'Py Torch [39]' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | On both datasets, we set the maximum perturbation as ϵ = 0.1, 0.05, 0.03 to keep inline with ILA. ... we run for 100 iterations on CIFAR-10 inputs and 300 iterations on Image Net inputs with a step size of 1/255 such that its performance reaches plateaus on both datasets. |