###### Abstract  
Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking. confidential information; screen-shooting; watermark; CNN; DCT; robustly +
Footnote †: journal: Medical Image and Image Processing  
keypoints, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD), which hybrid machine learning and traditional watermarking algorithms use to achieve screen-shooting-resilient watermarking. At present, the applicability of deep learning technology in the field of watermarking algorithm is growing rapidly, because this technology can effectively solve the process of watermark embedding and extraction [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. [PERSON] et al. [11] proposed a screen-shooting-resilient watermarking technique that jointly trains the scale distortion, blur, noise attack, JPEG compression, and color distortion attacks, while training the deep learning network to resist common printing, remakes, screen photography and other attacks. [PERSON] and [PERSON] [5] put forward an end-to-end watermarking strategy for the screen-shooting process. Datasets [6] were introduced by the framework when the camera and display are at high-perspective angles to guarantee significantly better results than existing schemes. [PERSON] et al. [7] offered a watermarking scheme with universal deep hiding (UDH) and proved that only adding a perspective transformation layer between the encoder and decoder can resist distortions introduced by the screen-shooting process to a certain extent. Compared with these schemes, a strategy was proposed as the template generation scheme for creating the watermark-embedded template and then embedding the watermark into the red and blue of the carrier image [8]. The trained deep learning network was applied to extract the watermark in the watermark extraction stage. [PERSON] et al. [9] subsequently established a watermarking scheme named \"TERA\". Similar to [8], the process of watermark embedding depends on a manual design named the superposition-based embedding scheme, and watermark extraction is designed using a deep neural network (DNN). The extraction algorithm is composed of enhancing, attention-guided [10] and regression networks. HiDDeN [13] applied neural networks are used to realize robust watermarking. Specifically, noise distortion layers are added to enhance their framework. We choose deep learning to construct an image watermarking framework that considers screen-shooting resilient watermarking complexity because of its recent outstanding performance on image watermarking tasks. [PERSON] et al. [3] proposed a robust watermarking technique via deep learning in the transform domain, but the method fails to resist screen-shooting attacks. [PERSON] et al. [11] put forward a screen-shooting resilient watermarking scheme in the spatial domain with minimal robustness. Spatial-domain-based watermarking is simple to implement, but it has less information embedded and poor robustness. Transform-domain-based watermarking can provide better embedding capacity and robustness under various attacks. We propose a screen-shooting-resilient watermarking algorithm based on [3; 11] using deep networks in the DCT domain.