# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 1 Introduction  
With the rapid development of digital technology and widespread adoption of social media platforms, protecting the copyright of digital images has become increasingly critical. The ease of digital content sharing and manipulation has led to growing concerns about the unauthorized use and distribution of copyrighted images, creating an urgent need for robust protection mechanisms. The visual quality of watermarked images is susceptible to adverse artifacts generated by image watermarking algorithms, which poses a challenge to the imperceptibility of the watermark. Robust image watermarking must address both visual quality and robustness challenges simultaneously. Traditional watermarking methods, operating in spatial and transform domains [1], often struggle to balance visual quality and robustness, especially when facing various image transformations commonly encountered in social media sharing. These classic approaches, including bit plane manipulation [2], directional filtering [3], and transformdomain techniques [4; 5; 6], have limitations in resisting complex image attacks, such as geometric distortions, compression, and filtering operations, frequently applied during content sharing and processing. Specifically, these methods heavily rely on handcrafted features and predefined transform domains [1], leading to inflexible solutions that are often optimized for specific scenarios. For example, spatial domain methods [2] mainly focus on basic pixel manipulations, while transform domain approaches [4; 5] are designed primarily for frequency-based attacks. This fragmented approach lacks the capability to provide comprehensive protection against diverse and evolving attack combinations commonly encountered in real-world applications. Recently, deep-learning-based watermarking approaches have shown promising results in addressing these challenges [7; 8; 9]. Notable works such as HiDDeN [10] and ReDMark [11] have demonstrated improved imperceptibility and robustness through end-to-end neural architectures. The emergence of transformer-based models like WFormer [12] has further advanced the field by leveraging attention mechanisms for watermark feature extraction. While existing deep learning watermarking frameworks have made significant progress, they still face several limitations. Most current methods employ encoder-decoder structures like StegaStamp [13] and MBRS [14], where the encoder often embeds redundant features unnecessary for the decoder, leading to suboptimal watermark embedding. Some approaches like De-END [15] attempt to address this through decoder-encoder coupling, but the fundamental challenge of efficient feature utilization remains. Additionally, these approaches typically treat watermark embedding as an isolated task, failing to leverage structural information inherent in images that could guide more natural and robust watermark placement. This is particularly relevant given the success of multitask learning [16; 17; 18; 19; 20] in various computer vision applications [21; 22]. Furthermore, although social media platforms have become the primary channels for image sharing, existing methods rarely consider the specific types of noise and transformations encountered in these scenarios, limiting their practical effectiveness. To address these challenges, we propose an edge-aware dual-task watermarking framework that incorporates structural features for robust watermark embedding. Our framework uniquely combines edge detection and watermark embedding through a maximally parameter-shared encoder, guided by knowledge distillation [23; 24] from an edge detection teacher. Unlike previous knowledge distillation applications that focus mainly on model compression [25] or performance enhancement [26; 27], we leverage this technique to enable structure-aware watermark embedding. To enhance robustness against real-world scenarios, we introduce a social network noise simulator that emulates various image transformations commonly encountered in social media platforms.