InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking

• URL: http://arxiv.org/abs/2506.20370v1
• Date: Wed, 25 Jun 2025 12:32:08 GMT
• Title: InvZW: Invariant Feature Learning via Noise-Adversarial Training for Robust Image Zero-Watermarking
• Authors: Abdullah All Tanvir, Xin Zhong,
• Abstract summary: This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning.<n>As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference signature through optimization in the feature space.
• Score: 1.4042211166197214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning. As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference signature through optimization in the feature space. The proposed framework consists of two key modules. In the first module, a feature extractor is trained via noise-adversarial learning to generate representations that are both invariant to distortions and semantically expressive. This is achieved by combining adversarial supervision against a distortion discriminator and a reconstruction constraint to retain image content. In the second module, we design a learning-based multibit zero-watermarking scheme where the trained invariant features are projected onto a set of trainable reference codes optimized to match a target binary message. Extensive experiments on diverse image datasets and a wide range of distortions show that our method achieves state-of-the-art robustness in both feature stability and watermark recovery. Comparative evaluations against existing self-supervised and deep watermarking techniques further highlight the superiority of our framework in generalization and robustness.

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