The rapidly evolving digital media space has given rise to several challenges for content creators, including the illegal duplication and distribution of video content. Forensic watermarking addresses these challenges by embedding imperceptible information in video content that can be extracted in case of an infringement to identify the exact source of leakage. However, watermarking algorithms sometimes suffer from geometric distortion attacks that desynchronize the location of the inserted watermarking.
To resist geometric distortion attacks, watermark synchronization must be followed. This process of synchronizing the location for watermark embedding and extraction is essential to design a robust watermarking solution. This location is often called a “patch”. Watermark locations can be synchronized by using the features of the video content, such as texture, edges, motion, etc. Features represent an invariant reference point for geometric distortion attacks such that referring features can solve synchronization problems. Feature-based watermarking solutions can thus be used to provide security against piracy to DRM protected content.
The first step for watermark insertion and detection is analyzing contents to extract features. The features are then relatively related to generate the patches. During the video watermarking insertion process, the watermark is inserted into all patches. During watermark detection, all patches are analyzed to detect the watermark. The ownership can be proved successfully if the watermark is correctly detected from at least one patch.
Thus, the distribution of feature points is a crucial factor in designing robust video watermarking solutions. In other words, the neighborhood size of feature points should be designed carefully. If the size is too small, the distribution of the feature points is concentrated on textured areas. On the other hand, if the size is too large, feature points become isolated. To obtain the homogeneous distribution of feature points, a circular neighborhood constraint is usually applied, in which the features points whose strengths are the largest are selected.
The watermarks are usually inserted in the regions considered most important to the video. The feature-regions are sometimes also detected by using the moving objects and the mosaic frames generated from the original video. The insertion in these regions maximizes the robustness of the watermarking algorithm since they represent the most important regions in the video and any degradation on these regions can cause a distortion of the whole video.