标题： ZK-WAGON：使用ZK-SNARKs的图像生成模型不可见水印 显示英文标题

标题： ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs

摘要： 随着图像生成模型变得越来越强大和易于获取，关于合成媒体的真实性、所有权和滥用问题的担忧已成为关键问题。 生成的图像与真实图像难以区分的能力带来了诸如虚假信息、深度伪造和知识产权侵权等风险。 传统的水印方法要么会降低图像质量，要么容易被移除，或者需要访问机密的模型内部信息 - 这使得它们不适合安全和可扩展的部署。 我们首次引入ZK-WAGON，这是一种使用零知识简洁非交互知识证明（ZK-SNARKs）对图像生成模型进行水印的新系统。 我们的方法能够在不暴露模型权重、生成提示或任何敏感内部信息的情况下，提供可验证的起源证明。 我们提出了选择性层ZK电路创建（SL-ZKCC），一种将图像生成模型的关键层选择性转换为电路的方法，显著减少了证明生成时间。 生成的ZK-SNARK证明通过最低有效位（LSB）隐写术不可察觉地嵌入到生成的图像中。 我们在GAN和扩散模型上展示了这个系统，为可信的人工智能图像生成提供了一个安全且与模型无关的流程。 显示英文摘要

摘要： As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals - making them unsuitable for secure and scalable deployment. We are the first to introduce ZK-WAGON, a novel system for watermarking image generation models using the Zero-Knowledge Succinct Non Interactive Argument of Knowledge (ZK-SNARKs). Our approach enables verifiable proof of origin without exposing model weights, generation prompts, or any sensitive internal information. We propose Selective Layer ZK-Circuit Creation (SL-ZKCC), a method to selectively convert key layers of an image generation model into a circuit, reducing proof generation time significantly. Generated ZK-SNARK proofs are imperceptibly embedded into a generated image via Least Significant Bit (LSB) steganography. We demonstrate this system on both GAN and Diffusion models, providing a secure, model-agnostic pipeline for trustworthy AI image generation.