# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 2 Related Work  
### Blind Image Watermarking  
Blind image watermarking is a technique used to embed a unique and invisible watermark into a digital image without needing the original image to detect. Due to the objective need for copyright protection, blind image watermarking has always been a popular research field [28; 29; 30]. Many classic watermarking schemes are based on spatial and transform domains to protect images [1]. Traditional watermarking methods have explored a variety of techniques used to embed and detect watermarks in digital images. For instance, the method described by [2] uses bit plane manipulation of the least significant bit (LSB) for easy and rapid decoding, and linear addition of the watermark to the image data for inherent security. [PERSON] and [PERSON] [3] introduce a new method for defining additive steganographic distortion in the spatial domain, using directional high-pass filters and the reciprocal Holder norm to guide the embedding process toward textured or noisy regions. [PERSON] et al. [4] combine discrete wavelet transform (DWT) and DCT to embed feature and tag information into the region of interest (ROI) and region of non-interest (RONI) of medical images, respectively. Another approach, as seen in [5], involves embedding a hash function into the three least significant bits of an image, providing both robustness and tamper localization. [PERSON] et al. [6] propose a block-based DCT coefficient modification technique, where the difference in discrete cosine transform (DCT) coefficients between two blocks is adjusted to embed the watermark. [PERSON] et al. [31] introduce a just-noticeable difference (JND) model that fuses cross-scale features from different sub-bands in the quaternionic DWT domain, enhancing the perceptual masking. [PERSON] et al. [32] use a feature detector to select local areas for embedding, apply SWT for denoising, and embed the watermark through histogram shifting, ensuring higher security and a larger capacity. However, classic watermarking schemes often struggle to resist various image attacks. In recent years, deep-learning-based watermarking technology has made significant contributions to copyright protection [7; 8; 9]. [PERSON] et al. [10] designed HiDDeN based on convolutional neural networks (CNNs), which is an auto-encoder-based watermarking network. Via joint training of the encoder-decoder as well as the differentiable noise layer, the encoded image is obtained, which has the ability to resist common image processing. ReDMark [11] is an adaptive diffusion watermarking framework that supports different required transforms to embed the watermark. The residual structure of the encoder with adjustable parameters is used to control the watermarking strength. Stegastamp [13] utilizes the reconstruction capability of U-net to resist various attacks on images. [PERSON] et al. [14] utilized a mini-batch of real and simulated JPEG compression (MBRS) for training. [PERSON] et al. [33] combined invertible and non-invertible (CIN) mechanisms to achieve visual quality and robustness. The above watermarking models employ the END structure; yet, the encoder often embeds many redundant features that are unnecessary for the decoder, since the encoder and the decoder are not satisfactorily coupled. To solve this drawback, [PERSON] et al. [15] presented a decoder-encoder-noise-decoder (De-END) structure, where the image is analyzed by the decoder at first for coupling with the encoder, and, consequently, redundant features are decreased to increase the watermarking performance. [PERSON] et al. [34] proposed SSLW to enhance noise resistance through self-supervised learning networks. [PERSON] et al. [35] designed Arwgan, utilizing attention-guided networks based on GANs to improve watermarking performance. More recently, [PERSON] et al. [36] proposed Flexmark with a message expansion layer to implement adaptive watermark embedding. [PERSON] et al.