A SVM-Based Zero-Watermarking Technique for 3D Videos Traitor Tracing

Abstract

The watermarking layer has a crucial role in a collusion-secure fingerprinting framework since the hidden information, or the identifier, directly attached to user identification, is implanted in the media as a watermark. In this paper, we propose a new zero watermarking technique for 3D videos based on Support Vector Machine (SVM) classifier. Hence, the proposed scheme consists of two major contributions. The first one is the protection of both the 2D video frames and the depth maps simultaneously and independently. Robust features are extracted from Temporally Informative Representative Images (TIRIs) of both the 2D video frames and depth maps to construct the master shares. Then, the relationship between the identifier and the extracted master shares is generated by performing an Exclusive OR (XOR) operation. The second contribution uses the SVM and the XOR operation to estimate the watermark. Compared to other zero watermarking techniques, the proposed scheme has proven good results of robustness and transparency even for long size watermarks, which makes it suitable for a tracing traitor framework.