DWT-Based Data Hiding Technique for Videos Ownership Protection

Abstract

This chapter proposes a wavelet data hiding scheme for video authentication and ownership protection. A watermark in the shape of a logo image will be hidden. In this research, a discrete wavelet transform (DWT) process is implemented using orthonormal filter banks, where the Y components of the YUV color space of the video frames are decomposed using DWT, and a watermark is inserted in one or more of the resulting sub-bands in a way that is fully controlled by the owner. Then, the watermarked video is reconstructed. The filters used for the DWT decompositions are randomly generated to increase the security of the algorithm. An enhanced detection technique is developed to increase the reliability of the system. The overall robustness of this scheme is measured when common attacks are applied to the test videos. Moreover, the proposed algorithm is used with the high-efficiency video coding (HEVC) technique to examine the whole performance. Furthermore, a selective denoising filter is built to eliminate the effect of the noise. The simulation results show that the proposed algorithm achieves well under both the visual and the metric tests. Moreover, it performed well against intentional and unintentional attacks. The average normalized correlation achieved is 97%, while the mean peak signal-to-noise ratio (PSNR) is 45 dB.

The Discrete Quincunx Wavelet Packet Transform

“…Wavelet is defined as a small wave that can be the base of all physical phenomena; which means that a time and/or space variation of a phenomenon is a sum of multiple wavelets. As examples, the wavelet transform was applied on an electrocardiogram (ECG) signal in order to extract the QRS complex [1] (time variation), on a video sequence in order to implement a hidden watermark [2] (time and space variation) and on a 2D image in order to reduce its size (compression) [3,4] (space variation). In this chapter, one considers the application of the wavelet on 2D image compression.…”

“…The term "lossless" refers to the conservation of all details in the image after reconstruction, which means that the original and reconstructed images are identical 2. The term "lossy" refers to the loss of details in the image after reconstruction by quantification or truncation, which means that the original image differs from the reconstructed one.…”