🤖 AI Summary
This work proposes a privacy-first mobile framework for verifying image authenticity in the face of escalating visual disinformation driven by generative AI. Addressing the challenge of balancing user privacy with on-device verification capabilities, the system introduces a novel multi-signal local verification mechanism that integrates encrypted image provenance tracking, generative model fingerprinting, and optional retrieval-augmented analysis—all executed directly on the user’s device. Built on a Rust/Flutter hybrid architecture, the framework enables high-privacy detection of AI-generated content and credibility scoring without uploading original data, thereby complying with regulatory requirements such as the EU AI Act. Furthermore, it interoperates with platform-level governance systems to deliver real-time, trustworthy verification capabilities to end users at the point of consumption.
📝 Abstract
The proliferation of generative AI poses challenges for information integrity assurance, requiring systems that connect model governance with end-user verification. We present Origin Lens, a privacy-first mobile framework that targets visual disinformation through a layered verification architecture. Unlike server-side detection systems, Origin Lens performs cryptographic image provenance verification and AI detection locally on the device via a Rust/Flutter hybrid architecture. Our system integrates multiple signals - including cryptographic provenance, generative model fingerprints, and optional retrieval-augmented verification - to provide users with graded confidence indicators at the point of consumption. We discuss the framework's alignment with regulatory requirements (EU AI Act, DSA) and its role in verification infrastructure that complements platform-level mechanisms.