QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
Authors: Thomas Paniagua, Ryan Grainger, Tianfu Wu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, the proposed Quad Attac K is tested in the Image Net-1k classification using Res Net-50, Dense Net-121, and Vision Transformers (Vi T-B and DEi T-S).4 Experiments |
| Researcher Affiliation | Academia | Thomas Paniagua, Ryan Grainger and Tianfu Wu Department of Electrical and Computer Engineering, NC State {tapaniag, rpgraing, twu19}@ncsu.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Details are provided in the Appendix A and our released code repository. |
| Open Datasets | Yes | In this section, we evaluate our Quad Attac K with K = 1, 5, 10, 15, 20 in the Image Net-1k benchmark [Russakovsky et al., 2015] using two representative pretrained Conv Nets: the Res Net-50 [He et al., 2016] and the Dense Net-121 Huang et al. [2017], and two representative pretrained Transformers: the vanilla Vision Transformer (Base) [Dosovitskiy et al., 2020] and the data-efficient variant DEi T (small) [Touvron et al., 2021]. |
| Dataset Splits | Yes | In Image Net-1k [Russakovsky et al., 2015], there are 50, 000 images for validation. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" and "mmpretrain package" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The selection of learning rates γ and λ values (see Eqn. 10) in the context of learning attacks requires careful consideration to achieve the desired trade-offs (see Fig. 3) and optimize the attack performance. If K < 5, the learning rate is set to γ = 0.75e 3 (for all the four models). For our Quad Attac K... we set λ = 0.5 for Quad Attac K and λ = 5 for the logit/probability space losses for the K = 1 case. For all other values of K, we use λ = 10... |