手机端本地验证图像真伪，保护隐私同时识别AI生成内容
Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection
- 用Rust+Flutter在手机本地完成图像溯源与AI检测
- 融合加密溯源、模型指纹等信号，给出可信度分级提示
- 符合欧盟法规，适合内容平台和普通用户防伪
生成式AI的泛滥威胁信息真实性，亟需将模型治理与终端验证结合。我们提出Origin Lens，一种以隐私为先的移动端框架，通过分层验证架构应对视觉误导信息。与依赖服务器的检测系统不同，该框架采用Rust/Flutter混合架构，在设备端完成加密图像溯源与AI检测。系统整合多种信号——包括加密溯源信息、生成模型指纹及可选的检索增强验证——在消费环节向用户提供分级可信度指示。本文还讨论了该框架如何契合欧盟《人工智能法案》与《数字服务法》要求，并作为平台级机制的补充，构建更完整的验证基础设施。
原文摘要 · Abstract (English)
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.
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