Hope Art
Official@hopeartorg · Vietnam
The purpose of Hope Art App is to protect digital artists' rights, prevent AI to train on arts without consent
Agent Skills by Hope Art
Showing 17 vetted skills indexed across 1 GitHub repositories.
HopeArtOrgmodel-distribution
Distribute ONNX models via Git LFS and GitHub Releases for Hope:RE.
HopeArtOrgtanstack-query
Fetch and mutate data in Svelte apps using TanStack Query and Tauri hooks.
HopeArtOrgprotection-algorithm
Apply SPSA-PGD adversarial perturbation to protect images from AI training.
HopeArtOrgtauri-command
Create Rust Tauri v2 commands with structured templates and error handling.
HopeArtOrgsvelte-component
Construct Svelte components following Hope:RE standards with Tailwind CSS.
HopeArtOrgonnx-export
Convert JAX/Python ML models to ONNX format via Google Colab notebooks.
HopeArtOrgfrontend-design
Develop distinctive frontend interfaces with unique design and functional elements.
HopeArtOrgonnx-rust-integration
Load and execute ONNX models in Rust with the ort crate.
HopeArtOrgui-ux-pro-max
Provide UI/UX design recommendations for web and mobile applications.
HopeArtOrgcontext7
Retrieve current software library documentation via curl queries.
HopeArtOrgsvelte-code-writer
Look up Svelte 5 and SvelteKit documentation and analyze code with CLI tools.
HopeArtOrgsvelte5-best-practices
Explain Svelte 5 runes for reactive state management and component development.
HopeArtOrgtypescript-advanced-types
Guide TypeScript developers through generics, conditional types, and mapped types.
HopeArtOrgponytail
Suggest minimal, efficient coding solutions for writing, refactoring, and reviewing code.
HopeArtOrgrust-best-practices
Review Rust code using Apollo GraphQL's idiomatic best practices.
HopeArtOrgchrome-devtools
Automate browser actions and debug web applications via Chrome DevTools.
HopeArtOrgcaveman
Compress AI model responses into caveman-style prose with six intensity levels.
Frequently Asked Questions About Hope Art
FAQPage SchemaHow does Hope Art prevent unauthorized training on digital images?▼
Hope Art utilizes SPSA-PGD adversarial perturbation to inject imperceptible noise into image files. This process creates a defensive layer that disrupts the feature extraction capabilities of training models, effectively protecting digital intellectual property from unauthorized ingestion without compromising visual quality for human viewers.
What technical stack is required to implement these protection measures?▼
Implementation requires a Rust environment for executing ONNX models via the ort crate and a Svelte-based frontend for asset management. Developers should be proficient in TypeScript generics and Tauri v2 command structures to integrate the perturbation logic into existing web or desktop applications.
Is the model protection technology open-source and cost-effective?▼
The protection logic is built on open-source standards, utilizing ONNX for model distribution and Rust for high-performance execution. There are no proprietary licensing fees for the core perturbation algorithms, allowing developers to integrate these defensive measures into their own projects using standard Git LFS and GitHub Releases.