1. Introduction

The digital age brought new technologies and services to people, industry, and governments. The digitization process covered all types of information being exchanged especially images and videos. The exchange of these forms of multimedia became faster and easier with the new communication network capabilities. On the other hand, that made it easier to steal or use these forms of multimedia illegally. Those concerns opened new horizons in the field of multimedia security especially data hiding [1]. Data hiding has many types; a useful classification is to divide data hiding into watermarking and steganography. New techniques used in video processing brought new challenges and difficulties to the data hiding methods. Moreover, the new compression techniques especially the high-efficiency video coding (HEVC) or H.265 are added to these challenges and difficulties to have reliable, secure, and robust data hiding methods [2, 3]. Various watermarking schemes that use different techniques have been proposed over the years [4–9]. To be effective, a watermark must be imperceptible within its host, extracted with ease by the owner, and robust in the face of both intentional and unintentional distortions [7, 10, 11]. In specific, discrete wavelet transform (DWT) has wide applications in the different areas of image and video processes such as compression, noise reduction, and watermarking [12]; this is attributed to its characteristics in space-frequency localization, multi-resolution representation, and superior human visual system (HVS) modeling [5]. The robustness is a very important aspect in data hiding or watermarking. To achieve the highest levels of robustness, new methods and techniques should be introduced and optimized at both the sender and receiver sides. Furthermore, the detection process should be enhanced to meet these requirements.

using more than one sub-band rather than a single sub-band is there; this method is

There are many scenarios that can be followed for the embedding process; one of them is to embed the data which is our binary watermark using a generated pseudorandom sequence [14]. This method depends mainly on doing the watermarking process by converting the original binary watermark image Q to some sort of a binary sequence S of a specific length M; in this case, the data pixels are given the value +1, whereas the background pixels are given the value �1. Furthermore, a pseudorandom sequence P of the same length M as our watermark sequence is generated using a secret key; likewise, this sequence is represented by values that are either +1 or �1. The DWT coefficients of the decomposed sub-bands that will be used for the hiding process are represented as a matrix Q<sup>1</sup> of the same size as our watermark. Moreover, it can be written as a vector T of length M. The binary watermark is hidden into this vector T, and that will result in a new vector that is

where α is a numerical factor which represents a weighting constant that determines the strength of the processed watermark. This number is chosen in such a way to offer a trade-off between the required robustness and the acceptable visual quality. Moreover, choosing this weighting factor should take into consideration many elements in image processing techniques such as the compression standard that is used and its intensity, the smooth features or the textures that are there in the image, and the algorithm that is followed when doing the detection process. Furthermore, how much energy content is there in the wavelet sub-bands must be considered at the hiding stage. One way to get the numerical magnitude factor is to have a comparison process between the energy of the original coefficients of the host DWT sub-band Q<sup>1</sup> and energy content of the original watermark image Q

α ¼ 2 ∗

(HH) bands. These bands offer better places for the hiding process.

ffiffiffiffiffiffiffiffiffiffiffiffi E Q<sup>1</sup> ð Þ E Qð Þ <sup>s</sup>

where E(Q1) represents the energy content of the original wavelet coefficients, while E(Q) represents the energy content of the watermark image Q; the energy was computed by taking the sum of the squared elements. The manipulated wavelet coefficients according to our hiding process are used then depending on their respective locations to reconstruct and build the watermarked image frame. The overall hiding process of a binary watermark for a Y frame is shown in Figure 1. It is clear from this figure, and this, in fact, depends on the decomposition structure that is followed that the low-low (LL) frequency area of the decomposed image is not used for our embedding process. This area or band is called the decimated image normally, and it results in both the pyramidal and DWT decompositions. It is clear that this band or image has most of the information or energy of the original image frame; the other images in other bands are normally called the error images, and they have lower energy content. In fact, they represent other bands depending on the analysis filters which are the low-high (LH), high-low (HL), and high-high

The watermark, which is primarily a binary image, can be embedded in any of the frames of the host video; moreover, the frames can be chosen in a fully controlled selective way. The degree of randomness that is achieved is up to the user

(2)

<sup>i</sup> ¼ ti þ α ∗ pi ∗ si, for i ¼ 1, 2 … M (1)

useful in having a robust method against the nonlinear collusion attack.

DWT-Based Data Hiding Technique for Videos Ownership Protection

DOI: http://dx.doi.org/10.5772/intechopen.84963

called T<sup>0</sup> according to the rule that is shown in this equation:

t 0

elements according to this empirical formula:

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In this research, a video watermarking process that depends on the discrete wavelet decompositions will be developed. Moreover, the detection process will be enhanced through statistical derivations. The security will be maintained through the adoption of random filter banks, the study of the motion and motionless scenes in the video frames, and the spread spectrum generation of the watermarks. The overall technique has to meet the requirements of visual quality, security, robustness, and computational complexity.
