A Novel Deep Learning Zero-Watermark Method for Interior Design Protection Based on Image Fusion
Yiran Peng, Qingqing Hu, Jing Xu, U KinTak, Junming Chen · Mathematics · 2025
Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design.