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

Alexander Loth; Dominique Conceicao Rosario; Peter Ebinger; Martin Kappes; Marc-Oliver Pahl

cs.CRFeb 3, 2026

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

Authors: Alexander Loth, Dominique Conceicao Rosario, Peter Ebinger, Martin Kappes, Marc-Oliver Pahl

Organizations: Frankfurt University of Applied Sciences Frankfurt am Main, Germany · Doctoral Center for Applied Computer Science (PZAI) Germany · IMT Atlantique, UMR IRISA, Chaire Cyber CNI Rennes, France

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 metadata-based AI detection locally on the device via a Rust/Flutter hybrid architecture. Our system integrates multiple signals - including cryptographic provenance, generative-model metadata and watermarks, 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.

Figures & tables

Panel of Figure 1 — Figure 1. Origin Lens architecture: Flutter UI, verification pipeline, FFI bridge, and Rust core. Cryptographic operations execute on-device below the processing boundary. Layered system architecture diagram with four stacked layers (Flutter UI, verification pipeline, cross-language FFI bridge, Rust cryptographic core) connected by downward arrows and separated by an on-device processing boundary.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 2 — Figure S1. Defense-in-depth verification layers. Primary layers (top) provide highest confidence; lower layers offer fallback signals when cryptographic provenance is unavailable. Inverted-pyramid diagram with four stacked layers of decreasing width: C2PA Provenance (top, widest), EXIF Metadata, SynthID watermarks, and Reverse Search (bottom, narrowest).

Panel of Figure 3 — Figure S2. C2PA manifest structure showing the relationship between assertions, claims, cryptographic signatures, and content hash bindings. Diagram showing the C2PA manifest structure with four stacked layers: Assertions, Claim, Signature (ECDSA/RSA), and Hard Binding (SHA-256), connected by arrows.

Panel of Figure 4 — Figure S3. Origin Lens hybrid architecture. The FFI boundary separates managed Dart code from native Rust, enabling memory-safe cryptographic operations. Layered architecture diagram showing four stacked components: Flutter UI Layer, Service Layer (Dart), FFI Bridge, and Rust Native Core (c2pa-rs), with arrows indicating data flow.

Panel of Figure 5 — Figure S4. Image analysis workflow showing decision points and four possible verification outcomes. Flowchart showing the image analysis process: Load Image leads to C2PA check, branching to manifest parsing and signature verification (Yes path) or EXIF analysis and AI detection (No path), with four possible outcomes: Verified, Invalid, AI Generated, or Warning.

Status	Color	Description
Verified	Green	Valid C2PA manifest with trusted certificate chain
AI Generated	Purple	Content identified as AI-generated via C2PA or EXIF markers
Warning	Orange	No manifest found, expired certificate, or parsing issue
Invalid	Red	Hash mismatch, broken certificate chain, or detected manipulation

Appendix

Table S1. Verification status indicators and their meanings.

Regulation	Relevant Requirements	Origin Lens Alignment
EU AI Act (2024/1689)	Article 50(2): Machine-readable marking of AI-generated content	C2PA manifest parsing and AI generation detection via metadata
GDPR (2016/679)	Article 25: Privacy by Design; Article 5(1)(c): Data minimization	On-device processing; no server transmission for core verification
Cyber Resilience Act (2024/2847)	Security-by-design for digital products	Rust memory safety; X.509 certificate validation
NIS2 Directive (2022/2555)	Supply chain security requirements	Local trust store; certificate chain verification

Appendix

Table S2. Regulatory alignment of Origin Lens features.

Panel of Figure 8 — Figure S5. Origin Lens user interface screenshots: (a) Dashboard with upload options (Files, Gallery, URL), (b) Verified result with valid C2PA manifest and trusted signature chain, (c) AI-generated content detected via Adobe Firefly metadata, (d) Parsing issue warning when signature chain is incomplete, (e) Detail view showing C2PA manifest history with timestamps and ingredient hashes, (f) Learn section with expandable FAQ on C2PA and AI detection. Six mobile app screenshots arranged in a 3x2 grid showing the Origin Lens interface: dashboard, verified result, AI-generated detection, parsing issue warning, edit history details, and FAQ section.

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Data Provenance Digital Forensics Robust Watermarking Generative AI Evaluation

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Generative artificial intelligence now synthesizes photorealistic imagery, audio, and video at a cost that defeats traditional forensic intuition. The legal consequences span three regimes studied so far in isolation: international operational law, domestic procedure, and product regulation. This article presents a unified evidentiary framework that maps cryptographic content provenance, robust statistical watermarking, and zero knowledge attestation to the proof requirements of each regime. We define a five tier threat model spanning naive regeneration, adversarial laundering, cross model regeneration, active watermark removal, and insider provenance forgery. We release a public benchmark of 12000 generated items across image, audio, and video modalities under six laundering pipelines for 72000 evaluation samples. We evaluate four representative schemes and report true positive rate at fixed false positive rate, robustness area under the curve, computational overhead, and a regime conditioned legal sufficiency score. We translate empirical detection bounds into legal sufficiency thresholds for command decisions under the law of armed conflict, for criminal and civil admissibility under domestic procedure, and for persistence audits under the European Union Artificial Intelligence Act and analogous regimes. The result is a reproducible reference pipeline, a public benchmark, and model annexes that lawyers, engineers, and operators can deploy together.

Abstract

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