Origin Lens: Reclaiming Trust on the AI-Mediated Web Through On-Device Image Provenance Verification

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Authors: Alexander Loth, Dominique Conceicao Rosario, Peter Ebinger, Martin Kappes, Marc‐Oliver Pahl
Cited by: 1
References: 23

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