With the rapid growth of generative AI and its widespread application in image editing, new risks have emerged regarding the authenticity and integrity of digital content. Existing versatile watermarking approaches suffer from trade-offs between tamper localization precision and visual quality. Constrained by the limited flexibility of previous framework, their localized watermark must remain fixed across all images. Under AIGC-editing, their copyright extraction accuracy is also unsatisfactory. To address these challenges, we propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction for robust copyright protection and tamper localization. OmniGuard employs a hybrid forensic framework that enables flexible localization watermark selection and introduces a degradation-aware tamper extraction network for precise localization under challenging conditions. Additionally, a lightweight AIGC-editing simulation layer is designed to enhance robustness across global and local editing. Extensive experiments show that OmniGuard achieves superior fidelity, robustness, and flexibility. Compared to the recent state-of-the-art approach EditGuard, our method outperforms it by 4.25dB in PSNR of the container image, 20.7% in F1-Score under noisy conditions, and 14.8% in average bit accuracy.

翻译：随着生成式人工智能的快速发展及其在图像编辑中的广泛应用，数字内容的真实性与完整性面临新的风险。现有的通用水印方法需要在篡改定位精度与视觉质量之间进行权衡。受限于先前框架的灵活性不足，其定位水印必须在所有图像中保持固定。在AIGC编辑场景下，其版权提取精度亦不理想。为应对这些挑战，本文提出OmniGuard——一种新型增强型通用水印方法，通过主动嵌入与被动盲提取相结合，实现鲁棒的版权保护与篡改定位。OmniGuard采用混合取证框架，支持灵活的定位水印选择，并引入退化感知的篡改提取网络以在复杂条件下实现精确定位。此外，设计轻量级AIGC编辑模拟层以增强全局与局部编辑的鲁棒性。大量实验表明，OmniGuard在保真度、鲁棒性与灵活性方面均表现优异。相较于当前最先进方法EditGuard，本方法在载体图像PSNR指标上提升4.25dB，在噪声环境下F1分数提升20.7%，平均比特准确率提升14.8%。