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Add model_transparency as a sandbox project
Signed-off-by: Mihai Maruseac <[email protected]>
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process/project-lifecycle-documents/model_signing_sandbox_stage.md
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## Application for creating a new project at Sandbox stage | ||
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### List of project maintainers | ||
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The project has 7 maintainers from 3 different organizations: | ||
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* Laurent Simon, Google, @laurentsimon | ||
* Daniel Major, NVidia, | ||
* Eoin Wickens, HiddenLayer, @EWickens | ||
* Mihai Maruseac, Google, @mihaimaruseac | ||
* Hayden Blauzvern, Google, @haydentherapper | ||
* Sarah Meiklejohn, Google, @smeiklej | ||
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### Mission of the project | ||
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The project must be aligned with the OpenSSF mission and either be a novel | ||
approach for existing areas, address an unfulfilled need, or be initial code | ||
needed for OpenSSF WG work. It is preferred that extensions of existing OpenSSF | ||
projects collaborate with the existing project rather than seek a new project. | ||
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> Create a cryptographic signing specification for artificial intelligence and | ||
> machine learning models, addressing challenges such as very large models that | ||
> can be used separately, and the signing of multiple disparate file formats | ||
> held within a directory. This specification may have wider applicability to | ||
> signing directories of multiple arbitrary file formats. This specification may | ||
> later be proposed as a formal standard. | ||
#### Specific Goals Include: | ||
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* Build a library for model signing and verification | ||
* Integrate said library with ML frameworks (e.g., TensorFlow, PyTorch) | ||
* Integrate said library with ML model hubs (e.g., Kaggle, Hugging Face) | ||
* Develop standards for efficient hashing of large models | ||
* Develop standards for efficient verification of models that contain multiple | ||
formats in the same place | ||
* Develop standards for efficient verification of models at inference time. | ||
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#### Non-Goals Include: | ||
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* Developing a new model format | ||
* Handling security of ML outputs | ||
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### OpenSSF Mission Alignment | ||
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We believe our mission aligns with the OpenSSF mission in the following ways: | ||
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> make it easier to sustainably secure the development, maintenance, and | ||
> consumption of the open source software | ||
The model signing project aims to reduce the complexity of signing and verifying | ||
the integrity of models, making it easier to be adopted by the industry at | ||
large. | ||
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> fostering collaboration | ||
The library is developed by a cohort of independent companies working together | ||
to solve common problems. The goal is to integrate the library with most tools | ||
that ML practitioners use, to uplift the entire ecosystem. | ||
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> establishing best practices | ||
The model signing library must be strongly tested. It should define standards | ||
for efficiently hashing and verifying integrity of models. | ||
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### AI/ML Security WG Alignment | ||
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This project started in parallel with the AI/ML Security WG. During one meeting | ||
of the WG, it was decided to spin up a SIG for model signing | ||
(https://github.com/ossf/ai-ml-security/issues/10). Since the output of this SIG | ||
is in code for this library and associated standards and specs, we need to make | ||
this a project. | ||
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### IP policy and licensing due diligence | ||
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When contributing an existing Project to the OpenSSF, the contribution must | ||
undergo license and IP due diligence by the Linux Foundation (LF). | ||
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* Yes (all code under Apache2 from the start, repo under Sigstore, which is | ||
already an OpenSSF (thus, LF) member) | ||
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### Project References | ||
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The project should provide a list of existing resources with links to the repository, and if available, website, a roadmap, contributing guide, demos and walkthroughs, and any other material to showcase the existing breadth, maturity, and direction of the project. | ||
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| Reference | URL | | ||
|--------------------|-----| | ||
| Main Repository | TODO | | ||
| Contributing guide | TODO | | ||
| Security.md | TODO | | ||
| Roadmap | TODO | | ||
| Demos | TODO | | ||
| Other | TODO | |