The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark, a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural network architectures and training strategies to embed imperceptible yet highly robust watermarks. InvisMark achieves state-of-the-art performance in imperceptibility (PSNR$\sim$51, SSIM $\sim$ 0.998) while maintaining over 97\% bit accuracy across various image manipulations. Notably, we demonstrate the successful encoding of 256-bit watermarks, significantly expanding payload capacity while preserving image quality. This enables the embedding of UUIDs with error correction codes, achieving near-perfect decoding success rates even under challenging image distortions. We also address potential vulnerabilities against advanced attacks and propose mitigation strategies. By combining high imperceptibility, extended payload capacity, and resilience to manipulations, InvisMark provides a robust foundation for ensuring media provenance in an era of increasingly sophisticated AI-generated content. Source code of this paper is available at: https://github.com/microsoft/InvisMark.

翻译：随着AI生成图像的广泛传播，对鲁棒内容认证方法的需求日益迫切。本文提出InvisMark——一种专为高分辨率AI生成图像设计的新型水印技术。该方法利用先进的神经网络架构与训练策略，嵌入不可感知且高度鲁棒的水印。InvisMark在不可感知性方面达到领先水平（PSNR$\sim$51，SSIM $\sim$ 0.998），同时在多种图像处理操作下保持超过97%的比特准确率。特别值得注意的是，我们成功实现了256位水印编码，在保持图像质量的同时显著提升了有效载荷容量。这使得嵌入带有纠错码的UUID成为可能，即使在严苛的图像畸变条件下也能实现近乎完美的解码成功率。本文还探讨了针对高级攻击的潜在脆弱性，并提出了相应的缓解策略。通过结合高不可感知性、扩展的有效载荷容量以及对图像操作的强韧性，InvisMark为日益复杂的AI生成内容时代提供了确保媒体溯源的坚实基础。本文源代码已公开于：https://github.com/microsoft/InvisMark。