# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
###### Abstract  
The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris-Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks. screen-cam process; local square feature region; synchronization; DFT; robust watermarking 23 displayed on the screen. In particular, the popularization of smartphones has made data leakage by capturing on-screen content much easier and common. However, verifying the copyright of a camera-captured image that is published without permission is still challenging. Digital image watermarking technology has been widely used for copyright protection and leakage tracking [12; 13; 14; 15; 16]. To hold someone accountable for the behavior of sneak shots, an invisible screen-cam robust watermark can be embedded in images. In this circumstance, the embedded watermark will survive in a camera-captured image. To design a screen-cam robust watermarking scheme that can extract watermark information from a single screen-captured photo, we need to analyze the screen-cam process first. The screen-cam process can be considered a compound attack containing moire noise, luminance distortion, and geometric distortion. Consequently, most traditional watermarking methods are not applicable to the screen-cam process. The process of generating a new image by camera capture is referred to as a cross-media information transfer process that contains analog-to-digital and digital-to-analog conversion attacks. Thus, the print-scan and print-cam processes have certain similarities with the screen-cam process. Many researchers have focused on extracting watermark information from printed images. Based on embedding domains, these methods can be divided into spatial domain-based and frequency domain-based methods. The spatial domain-based algorithms primarily employed watermark patterns as watermark information. [PERSON] et al. [17] proposed a sinusoidal watermark pattern-based adaptive additive embedding algorithm. The pattern-based method was continuously improved with a frame detection method [18] to achieve autocorrection during extraction, a new pattern design [19; 20; 21] to achieve better imperceptibility and robustness, or a combination with computational photography technology [22; 23] to solve the lens focusing problem. However, these pattern-based watermark methods are weak to cropping attacks, and the performance of watermark detection will decrease if an image is zoomed-out before it is printed. The frequency domain-based algorithms include Fourier domain-based methods and wavelet domain-based methods.