Origin Lens: Reclaiming Trust on the AI-Mediated Web Through On-Device Image Provenance Verification
Abstract
Generative AI enables synthetic visual media at a scale that exceeds human verification capacity. This widens an asymmetry on the Web: users encounter images of uncertain provenance with few practical means of assessment. We present Origin Lens, a privacy-first mobile framework that performs cryptographic image provenance verification and AI detection entirely on-device. Built on a Rust/Flutter hybrid architecture, the system combines multiple verification signals—C2PA content credentials, generative model fingerprints, and optional retrieval-augmented context—to provide users with graded confidence indicators at the point of consumption. Unlike centralized platform moderation, this approach allows individuals to verify visual content independently. We discuss how client-side verification infrastructure relates to EU regulatory frameworks (AI Act, DSA) and can complement platform governance for information integrity on the Web.
Fields of Study & Subjects
Scientific Computing and Data Management
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- Companion Publication of the 2026 18th ACM Web Science Conference
- Publisher
- ACM
- Pages
- 1–5
- Publication date
- 23 May 2026