Geometric Image Synchronization with Deep Watermarking

2025-09-18

AI Summary

To address the degradation of watermark synchronization under geometric transformations (e.g., rotation, cropping), this paper proposes SyncSeal—a fully end-to-end trainable robust watermarking framework. Methodologically, SyncSeal jointly optimizes an embedding network and an extraction network that explicitly model and regress geometric transformation parameters; integrates an adversarial discriminator to preserve visual fidelity; and supports plug-and-play integration with existing watermarking schemes. By synergistically optimizing regression loss for transformation estimation and adversarial loss for perceptual quality, SyncSeal achieves accurate geometric parameter recovery and precise image registration under diverse geometric and photometric attacks. Experimental results demonstrate that SyncSeal significantly enhances detection robustness and synchronization accuracy of state-of-the-art watermarking methods under severe geometric distortions—including rotations within ±30° and arbitrary cropping up to 50%—while maintaining high visual quality.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

Synchronization is the task of estimating and inverting geometric transformations (e.g., crop, rotation) applied to an image. This work introduces SyncSeal, a bespoke watermarking method for robust image synchronization, which can be applied on top of existing watermarking methods to enhance their robustness against geometric transformations. It relies on an embedder network that imperceptibly alters images and an extractor network that predicts the geometric transformation to which the image was subjected. Both networks are end-to-end trained to minimize the error between the predicted and ground-truth parameters of the transformation, combined with a discriminator to maintain high perceptual quality. We experimentally validate our method on a wide variety of geometric and valuemetric transformations, demonstrating its effectiveness in accurately synchronizing images. We further show that our synchronization can effectively upgrade existing watermarking methods to withstand geometric transformations to which they were previously vulnerable.

Problem

• Robust image synchronization against geometric transformations
• Enhancing watermarking methods' geometric transformation robustness
• End-to-end training for accurate transformation parameter prediction

Innovation

• Deep watermarking method for geometric synchronization
• End-to-end trained embedder and extractor networks
• Upgrades existing methods against geometric transformations