
## Defending Against Adversarial Attacks via Neural Dynamic System
Xiyuan Li, Zou Xin, Weiwei Liu
Keywords: 
NeurIPS/2022/Proceedings/54095 - Defending Against Adversarial Attacks via Neural Dynamic System.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

Implementation cannot be found. In the checklist the authors marked the question regarding code as N/A. A repository is linked the authors state they use.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[3]

(2/2)

The authors use CIFAR-10 and MNIST in section 6. Citations provided on both. The authors provide a direct link to the CIFAR repository. No descriptions/statistics.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[9]

The parameter values of alpha 1 and 2 are stated in 6.1. However many more parameters are stated in algorithm 1 of the appendix. These are almost not specified.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[2]

Metrics are classifcation accuracy. Standard dataset splits are used in the experiments (sec 6). It is not clear what data subset the results are reported on. Results are single model attacks.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[4]

Requries expertise on adversarial attacks.
