https://github.com/amazon-science/detecting-adversarial-samples-using-sagemaker
https://github.com/amazon-science/detecting-adversarial-samples-using-sagemaker
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (8.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: amazon-science
- License: mit-0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 434 KB
Statistics
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Detecting adversarial samples using SageMaker ModelMonitor and Debugger
This repository contains the files for the blogpost "Detect adversarial inputs using Amazon SageMaker Model Monitor and Amazon SageMaker Debugger".
Create a SageMaker notebook instance and clone the repository:
git clone https://github.com/amazon-research/detecting-adversarial-samples-using-sagemaker.git
In the notebook Detectingadversarialsamples.ipynb we first train an image classification model (ResNet18) on CIFAR10 and then deploy it on Amazon SageMaker.
We will setup a custom SageMaker Model Monitor schedule that periodically kicks off a custom processing job that will run a two-sample statistical test using MMD (maximum mean discrepancy). This test detects adversarial samples.
The image below shows, the TSNE visualizations of feature representations for natural and adversarial samples obtained from different layers in the model (layer 0 presents the model inputs). We can see that adversarial samples become more distinguishable for the deeper layer of the ResNet18 model. The intuition is, that raw inputs are noisy and high-dimensional whereas the latent representation (produced by the deeper layer of a neural network) capture low-dimensional semantic information. We will use SageMaker Debugger in the endpoint to capture these representations during inference.
We will then run inference with test and adversarial images and determine how well they are detected by our custom Model Monitor.
Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Owner
- Name: Amazon Science
- Login: amazon-science
- Kind: organization
- Website: https://amazon.science
- Twitter: AmazonScience
- Repositories: 80
- Profile: https://github.com/amazon-science
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: over 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0