## 1 Introduction  
In contrast to cryptography, which primarily focuses on ensuring message confidentiality, digital watermarking places greater emphasis on copyright protection and tracing [1; 2]. Classical watermarking involves the covert embedding of a watermark (a sequence of data) within media files, allowing for the extraction of this watermark even after data distribution or manipulation, enabling the identification of data sources or copyright ownership [3]. However, this embedding process necessarily involves modifications to the host data, which can result in some degree of degradation to data quality and integrity. In response to the demand for high fidelity and zero tolerance for data loss, classical watermarking has been supplanted by zero-watermarking. Zero-watermarking focuses on extracting robust features and their fusion with copyright information [4; 5]. Notably, a key characteristic of zero-watermarking lies in the generation or construction of the zero-watermark itself, as opposed to its embedding. Commonly, zero-watermarking algorithms are traditionally reliant on handcrafted features and typically involve a three-stage process. The first stage entails computing robust features, followed by converting these features into a numerical sequence in the second stage. The third stage involves fusing the numerical sequence with copyright identifiers, resulting in the generation of a zero-watermark without any modifications to the original data. Notably, the specific steps and features in these three stages are intricately designed by experts or scholars, thereby rendering the performance of the algorithm contingent upon expert knowledge. Moreover, once a zero-watermarking algorithm is established, continuous optimization becomes challenging, representing a limitation inherent in handcrafted approaches. Introducing deep learning technology is a natural progression to overcome the reliance on expert knowledge and achieve greater optimization in zero-watermarking algorithms. Deep learning has recently ushered in significant transformations in computer vision and various other research domains [6; 7; 8; 9]. Numerous tasks, including image matching, scene classification, and semantic segmentation, have exhibited remarkable improvements when contrasted with classical methods [10; 11; 12; 13; 14]. The defining feature of deep learning is its capacity to replace handcrafted methods reliant on expert knowledge with Artificial Neural Networks (ANNs). Through training ANNs with ample samples, these networks can effectively capture the intrinsic relationships among the samples and model the associations between inputs and outputs. Inspired by this paradigm shift, the zero-watermarking method can also transition towards an end-to-end mode with the support of ANNs, eliminating the need for handcrafted features. In addressing watermark performance, conventional handcrafted methods encounter challenges in simultaneously enhancing robustness and discriminability. To address this limitation and achieve concurrent improvements, this paper proposes a novel zero-watermarking network named ZWNet, which employs distinct strategies. Firstly, ZWNet combines ConvNeX and LK-PAN to enhance the extraction of local features and consider the global context more comprehensively. Secondly, the watermark block is strategically designed as the leading component, integrating with copyright information, encrypting the watermark, and generating a distinctive image identifier. Thirdly, the training strategy for ZWNet focuses on the image identifier it produces. To enhance robustness, ZWNet is trained to yield the same image identifier for both the original and attacked images. Simultaneously, to improve discriminability, ZWNet is trained to produce different image identifiers for distinct images.