I’ve been experimenting with ways to protect digital assets from automated AI web scrapers. I built Sigil, a local-first desktop application that embeds cryptographically signed ownership IDs into the LSB (Least Significant Bit) layer of images.
The desktop vault itself (Svelte/Tauri/SQLite) is closed-source to protect the HMAC-SHA256 signing architecture, but I have open-sourced the Rust extraction standard today.
The idea is to create an open protocol where AI data procurement teams can run the extractor against their scraped datasets. If it detects a payload, they know the asset is cryptographically locked and requires a license.
The extractor logic relies entirely on image and hex crates for memory-safe pixel parsing. I'd love any feedback on the extraction architecture or the LSB approach.
I’ve been experimenting with ways to protect digital assets from automated AI web scrapers. I built Sigil, a local-first desktop application that embeds cryptographically signed ownership IDs into the LSB (Least Significant Bit) layer of images.
The desktop vault itself (Svelte/Tauri/SQLite) is closed-source to protect the HMAC-SHA256 signing architecture, but I have open-sourced the Rust extraction standard today.
The idea is to create an open protocol where AI data procurement teams can run the extractor against their scraped datasets. If it detects a payload, they know the asset is cryptographically locked and requires a license.
The extractor logic relies entirely on image and hex crates for memory-safe pixel parsing. I'd love any feedback on the extraction architecture or the LSB approach.
Extractor Repo: https://github.com/nishal21/Sigil-extractor Landing Page (Astro): https://nishal21.github.io/Sigil-extractor/