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..
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
Authors: Mingkang Zhu, Tianlong Chen, Zhangyang Wang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct comprehensive experiments with diverse setups to validate the effectiveness of proposed homotopy algorithm on the CIFAR-10 (Krizhevsky, 2009) and the Image Net (Deng et al., 2009) datasets. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin, USA. |
| Pseudocode | Yes | Algorithm 1 Our Subroutine for Initial Weight Search (Lambda Search) and Algorithm 2 The Homotopy Attack Algorithm |
| Open Source Code | Yes | Our codes are available at: https://github.com/ VITA-Group/Sparse ADV_Homotopy. |
| Open Datasets | Yes | Extensive experiments on the CIFAR-10 (Krizhevsky, 2009) and Image Net (Deng et al., 2009) endorse the superiority of our new homotopy attack. |
| Dataset Splits | Yes | For nontargeted attack, we randomly select 5000 images from the test set of CIFAR-10, and 1000 images from the validation set of Image Net as the input images. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU models, or cloud computing instances with specifications) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., libraries, frameworks, or programming languages) used in the experiments. |
| Experiment Setup | Yes | Since we are highly interested in generating sparse and invisible adversarial perturbations while not extremely sparse but visible ones, we maintain a relatively small ℓ -norm of generated perturbations. That is, we set ϵ to 0.05, which is a relatively small number in the [0, 1] range of a valid image. ... The confidence parameter κ is set to 0. |