Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
Authors: Mingkang Zhu, Tianlong Chen, Zhangyang Wang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |