## 2 Related Work
### Zero-Watermarking  
The concept of zero-watermarking in image processing was originally introduced by [PERSON] et al. [15]. This technology has garnered significant attention and research interest due to its unique characteristic of preserving the integrity of media data without any modifications. Taking images as an example, the zero-watermarking process can be broadly divided into three stages. The first stage involves the computation of robust features. In this phase, various handcrafted features such as Discrete Cosine Transform (DCT) [16; 17], Discrete Wavelet Transform (DWT) [18], Lifting Wavelet Transform [19], Harmonic Transform [5], and Fast Quaternion Generic Polar Complex Exponential Transform (FQGPCET) [20] are calculated and utilized to represent the stable features of the host image. The second stage focuses on the numerical conversion of these features into a numerical sequence. Mathematical transformations such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are employed to filter out minor components and extract major features [16; 21]. The resulting feature sequence from this stage serves as a condensed identifier of the original image. However, this sequence alone cannot serve as the final watermark since it lacks any copyright-related information. Hence, the third stage involves the fusion of the feature sequence with copyright identifiers. Copyright identifiers can encompass the owner's signature image, organization logos, text, fingerprints, or any digitized media. To ensure the zero-watermark cannot be forged or unlawfully generated, cryptographic methods such as Advanced Encryption Standard (AES) or Arnold Transformation [22] are often utilized to encrypt the copyright identifier and feature sequence. The final combination can be as straightforward as XOR operations [16]. Consequently, the zero-watermark is generated and can be registered with the Intellectual Property Rights (IPR) agency. Additionally, copyright verification is a straightforward process involving the regeneration of the feature sequence and its comparison with the registered zero-watermark. The process of zero-watermarking technology is illustrated in Figure 1.  
Presently, several research efforts focus on deep-learning-based watermarking methods, encompassing both classical watermarking of embedding style and zero-watermarking of generative style. In the domain of embedding-style watermarking, a method inspired  
Figure 1: The process of zero-watermark generation and verification. by the architecture of Autoencoder has been proposed. In this approach, Autoencoders encode the watermark and embed it using convolutional networks. For watermark extraction, Autoencoders are also employed to extract and decode the watermark [23]. Other Autoencoder-based methods aim to enhance robustness or improve efficiency [24; 25]. Despite their superior performance in robustness and elimination of reliance on prior knowledge, embedding-style watermarking significantly differs from zero-watermarking methods, as the latter maintains the original data unchanged. Additionally, it is noteworthy that zero-watermarking places greater emphasis on discriminability, a focus less pronounced in embedding-style watermarking. In the realm of deep-learning-based zero-watermarking methods, a hybrid scheme that combines traditional Discrete Wavelet Transform (DWT) and the deep neural network ResNet-101 has been proposed. This approach involves applying DWT to the host image and subsequently sending the wavelet coefficients to ResNet-101 [26]. While exhibiting strong robustness against translation and clipping, this scheme falls short of being an end-to-end solution. Regarding end-to-end zero-watermarking, some studies employ Convolutional Neural Networks (CNN), VGG-19 (developed by the Oxford Visual Geometry Group), or DenseNet to generate robust watermark sequences [27; 28; 29]. Another line of research predominantly revolves around the concept of style transfer [30].