# Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection

arXiv: https://arxiv.org/abs/2602.03423 · HTML: https://mlsanity.com/paper/2602.03423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [SynthID-Image: Image watermarking at internet scale](https://mlsanity.com/paper/2510.09263/) (2025)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [Comprint: Image Forgery Detection and Localization using Compression Fingerprints](https://mlsanity.com/paper/2210.02227/) (2022)
- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation](https://mlsanity.com/paper/2211.04775/) (2022)
- [Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild](https://mlsanity.com/paper/2601.22871/) (2026)
- [PROVES: Establishing Image Provenance using Semantic Signatures](https://mlsanity.com/paper/2110.11411/) (2021)
- [A Study of Undefined Behavior Across Foreign Function Boundaries in Rust Libraries](https://mlsanity.com/paper/2404.11671/) (2024)
- [RealSeal: Revolutionizing Media Authentication with Real-Time Realism Scoring](https://mlsanity.com/paper/2411.17684/) (2024)
- [Veracity: An Open-Source AI Fact-Checking System](https://mlsanity.com/paper/2506.15794/) (2025)

## Built on by (most cited first, 2 of 2)

- [Authenticated Contradictions from Desynchronized Provenance and Watermarking](https://mlsanity.com/paper/2603.02378/) (2026)
- [Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News](https://mlsanity.com/paper/2604.03755/) (2026)

## Abstract (arXiv)

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.