# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 3 Proposed Watermarking Scheme
### Local Square Feature Region Construction  
Due to the desynchronization attacks caused by the screen-cam process and user operations, we need to develop an appropriate synchronization method to locate the watermark. We test the feasibility of the Harris-Laplace, SIFT, and SURF operators, which are extensively employed to construct local scale-invariant feature regions (LFRs) as message embedding areas [37; 38; 39; 40; 41; 42; 43; 44; 45], in the screen-cam process. To select the most suitable operators for LFRs construction, the variations of feature point coordinates, feature scale, and feature direction are quantitatively analyzed under different shooting distances. The images we used here are shown in Figure 2. All host images are \(1024\times 1024\) pixels. Because of the blurring of the image edges caused by the low-pass filtering attack and the lens distortion, it is difficult to restore the captured image to exactly correspond to the original image. Inevitably, there will be a displacement between the coordinates of the corresponding pixels. Therefore, the feature points are considered to be repeated when the offsets of their coordinates are smaller than five pixels. Besides, considering the requirements of watermark synchronization, the feature scale variation should be below 10% at the same time. Furthermore, the feature points at the edge of the image are excluded. In order to reduce the impact of noise, we perform a Gaussian filter on both the original images and the captured images. As shown in Figure 3a, after a Gaussian function, we discover that the middle- and high-scale, which means the feature scale is greater than 15, feature points of the Harris-Laplace or SIFT operators can achieve high repeatability. The repeatability here refers to the ratio of the number of feature points extracted after a screen-cam attack and satisfying the above-mentioned pixel offset and scale variation criteria, compared to the original number of feature points. We also note that although the SIFT operator has a better performance at a long shooting distance, it does not work well at a close shooting distance, which indicates that it is more sensitive to moire noise. Comparatively, the Harris-Laplace operator is more stable at different shooting distances, which is more suitable for watermark synchronization. Regarding the feature orientation descriptors, we note that the SURF orientation descriptor is more robust than the SIFT to the screen-cam process, where the orientation variations of repeated SURF feature points are predominantly less than five degrees, as shown in Figure 3b. We considered that the integral image and Haar wavelet-based SURF orientation descriptor is more robust to the blurring and luminance change in the screen-cam process than a Gaussian image and histogram-based SIFT orientation descriptor.  
Figure 2: Host images: Lena, Baboon, Airplane, Peppers, Building, Pentagon, White House, and Naval Base.  
Therefore, in our method, a modified Harris-Laplace detector and SURF orientation descriptor are integrated to construct the RST invariant LFRs. To increase the detection rate of feature points during the screen-cam process, we also employ a Gaussian function.  
In previous LFR-based methods, circular feature regions are commonly constructed. These regions will involve zero-padding [43] or rearrangement [46] to a square region before message embedding, which will cause further distortions [47]. Therefore, we directly construct LSFRs. Figure 4 illustrates the subprocesses. The details are as follows.