Digital Watermarking Basic Principles Computer Science Essay

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The project begin with the learning process and studies conducted by previous researcher. This chapter gives an overview regarding the theory that is applied into this project and to familiarize the reader with several concepts of watermarking.

2.2 Digital Watermarking Basic Principles

Digital watermarking is a method that allows an individual to add secret information such as copyright notices, company's logo, or other verification messages into digital media content such as audio, video or image signals and documents without degrading the overall quality of digital content. The secret information named as watermark in watermarking field.

Figure 1 depicts a general digital watermarking system.

Figure 1: A general digital watermarking system.

The inputs to the embedding watermark process are the watermark, the cover object and a secret key. The key is used to enforce security and to protect the watermark. The watermarked image is the media content that contains the watermark.

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Later, the watermark can be detected or extracted to make an assertion about the data.

The channel for the watermarked data could be a lossy, noisy, unreliable channel. Thus the received data may be different from the original watermarked data. The inputs for extraction are the received watermarked data and the key corresponding to the embedding key. The output of the watermark extraction process is the recovered watermark and digital media content [1], [2], [3] and [5].

The stages of watermarking process are described in the following subsections.

2.2.1 Embedding Stage

In this embedding stage, the original image (H) to be watermarked is preprocessed before embedding a watermark (W). Early watermarking schemes worked in the spatial domain, where the watermark is added by modifying pixel values of the host image.

Examples of such techniques are Substitution Watermarking and Additive Watermarking [9]. For the case of embedding in the transform domain, this may involve converting the image to the desired domain such as the Discrete Cosine Transform (DCT), the Discrete Fourier transform (DFT) and the Wavelet Transform (WT) domains. Then, to get the watermarked image, inverse transform is performed.

The watermark to be embedded can be a binary image, a bit stream or a pseudo-random number. The key is used to generate a more secure watermark. The watermark key is private and only the authorized person is known. It ensures that only authorized person are able to detect the watermark data.

Figure 2.2 illustrates the encoding process.

Figure 2.2 Watermark Embedding

Mathematically, this can be written as

E (H, W, K) = H* (2.1)

Where E is an encoder function and K is the secret key.

The output is the watermarked data. It is perceptually identical to H and is obtained by performing an inverse transform on the altered transform coefficients.

It is passes through the transmission channel. The digital watermarked product will be transmitted through some ways such as internet, or transmission within pen-drive. The channel for the watermarked data could be lossy, noisy and reliable channel. In the process of transmission and distribution of the watermarked image, this will contribute errors to the watermarked image. All these manipulations on the watermarked image have to be seen as an attack on the embedded information. Thus the received watermarked data may be different from the original watermarked data. Details of the attacks will be described in section 2.6.

2.2.2 Extracting Stage

The extraction watermark is the opposite of the embedding process. At the receiving side, the receiver extracts data and recovers the original image from watermarked image. Figure 2.3 illustrates the decoding process.

Figure 2.3 Watermark Extracting

Firstly, D is denoting as a decoder function. D takes a watermarked image H* whose ownership is to be determined and recovers a watermark W* from the image using the secret key (K).

This watermarking technique is said to be secure since the key used at embedding, is needed for extraction. It is difficult to remove or alter the message from the data without knowing the key.

Mathematically, this is written as

D (H*, H, K) = W* (2.2)

Where D is a decoder function and K is the secret key.

The extracted watermark (W*) will be used in the decision making stage. The watermarking system examines the extracted data by evaluating the similarity between the original watermark image (W) and the extracted watermark image (W*) during this decoding stage.

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2.2 Watermarking Classification

The classification of watermarking is shown in Figure 2.4 underlines a number of interesting characteristics of a variety of watermarking techniques.

Figure 2.4: Types of Watermarking

There are a lot of categories in watermarking systems that are designed for different applications. Among them, robust watermarks are usually used for copyright protection in order to declare rightful ownership because it is designed to survive from any kind of intentional or unintentional alteration. As example in ownership and identification application, a robust watermark containing identification information of owner was embedded into the host images. If she know that one of her image have been edited or published illegally, she can use the watermark as prove of her belongings [3] and [9].

Contrary, fragile watermarks are applied to content authentication and integrity verification because they are fragile to attacks [3] and [5]. In [9], [10] and [27], a good image authentication watermark will destroy if the watermarked image is manipulated in the slightest manner, which can be used for image authentication. In medical diagnosis, military communication or forensic applications, authenticity of source media are important. A fragile watermark was embedded into the host media containing source secret information of media content. Only authorized recipients are able to verify the integrity of the content by detecting the watermark information. If the watermarked data have been manipulated the embedded watermark becomes undetectable. Thus, the recipient will know that the media is not trustworthy.

The image watermarking algorithms can be categorized into two categories: spatial - domain watermarks and frequency - domain techniques. The spatial - domain techniques directly modifying values of some selected pixels. Least significant bit (LSB) is often used in spatial - domain technique. On the other hand, frequency - domain techniques modify the values of some transformed coefficients such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Wavelet Transform [9], [11], and [16].

Human perception also plays an important role in the watermarking technique. It can be divided into two categories, visible watermarking and invisible watermarking. Visible watermarks are an extension of the concept of logos. Usually those authorities will insert their logos as the watermark on the digital multimedia content such as videos or images. The disadvantages of visible watermarks are degrading the quality of image and detection by visual only. Thus, it is not possible to detect them by existence removal watermark software [31] and [32]. On the other hand, invisible watermarking technique is a different technique used to embed invisible information or message in the products. The main difference compared to visible watermarking is the watermark will be hidden inside the product and cannot be identified by naked eyes. It can be detected by an authorized agency only. Such watermarks are usually used for content or author authentication and for detecting unauthorized copier.

Figure 2.5 (a) and (b) shows an example of Lena image in visible watermarking and invisible watermarking.

Visible Watermarking (b) Invisible Watermarking

Figure 2.5: Examples of Invisible Watermark and Invisible Watermark.

2.3 Watermarking Design Issues

The requirement of a watermarking system strongly relies on the particular applications in which it will be deployed. The main requirements which should be fulfilled of digital watermarking system in authentication fields are imperceptibility, reversibility and fragility [9], [16], [24] and [29].

2.3.1 Imperceptibility

The watermarking design should be perceptually invisible so data quality is not degraded and attackers are prevented from finding and deleting it. It is called imperceptible if the watermarked image is perceptually equivalent to the original watermark information

Measuring imperceptibility in watermarking is important to measure the image quality at the decoder stage. In addition, Peak Signal-to-Noise Ratio (PSNR) is used to indicate the quality of image with watermarking process. Higher PSNR values imply closer similarity between reconstructed and original image.

The formula is showed below

(2.3)

Where M-N is image size, 255 is gray level range of image, M (i, j) and M' (i, j) are gray level values at pixel (i, j) of original image and watermarked result image respectively. Mean Square Error (MSE) value is the sum between original image and watermarked image in dB unit.

2.3.2 Robustness

Reversibility is defined as perfectly reconstruction, where the original host image is perfectly recovered after the marked image passes the authentication process. The reversibility of the algorithm can be evaluated by the similarity measurements Normalized cross-correlation (NCC) between the original watermark and the extracted watermark. These are the measurement to evaluate the quality of reversibility which capable to retrieve the exact original images perfectly after the extraction process.

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NCC is calculated with the formula:

(2.4)

Where NCC is Normalized Cross Correlation

2.3.3 Fragility

As explained by Ton Kalker [6]; watermarking security is the inability by unauthorized users to have access to the raw watermarking channel. In other words, watermark security refers to the failures of unauthorized users to alter, to remove, to read or to write the watermark content established by robust watermarking. Fragility means the embedded watermark is expected to be destroyed when the attacks are added on the host image [3], [10] and [29]. Fragile watermarks are applied to content authentication and integrity verification because they are fragile to attacks [3] and [5]. In [9], [10] and [27], a good image authentication watermark will destroy if the watermarked image is manipulated in the slightest manner, which can be used for image authentication.

2.4 Existing Image Watermarking Techniques

The classifications of watermarking algorithms are done in several view points. One of the viewpoints is based on processing domain spatial domain or frequency domain.

2.4.1 Spatial Domain Techniques

Most of the early researches in digital watermark embedded the watermark in the spatial domain which is straightforward, simple and not costly. Least Significant Bit (LSB) is the easiest technique in the spatial domain. Techniques in spatial domain commonly share the following characteristics:

The watermark information is applied in the pixel domain.

No transforms are applied to the cover object in watermark embedding.

Combination with the cover object is in the pixel domain.

The correlation is calculated between the expected patterns with the received signal.

Generally, spatial domain watermarking techniques are not robust against image processing operations because the embedded watermark is not distributed for the whole image and thus easy to destroy the watermark [2].

Least Significant Bit (LSB) Technique

The easiest method of watermark embedding is to embed the watermark into the least significant bits of the cover object. In grayscale image, the most significant bit (MSB) is in the left side and the least significant bit (LSB) to the right of 8 bits of a pixel.

Figure 2.6 (a) shows a pixel having the gray value 130. The idea of LSB is to replace the LSB of a pixel with the watermark. As shown in Figure 2.6 (b), the value changes from 130 to 131 when the LSB is changed. It is undetectable from human eyes. If the bit is position more closely to MSB, the image will be more distorted, as described in Figure 2.6 (c). The most serious disadvantage of spatial-domain technologies is limited robustness due to can't survive from attacks such as lossy compression and low-pass filtering [9].

Figure 2.6 (a) 8 bit pixels with a value of 130. (b) The value changed to 131 after replacing the LSB. (c) The value is changed to 2 after the MSB substitution

2.4.2 Frequency Domain Techniques

An alternative for spatial -domain is Frequency domain, in order to have a more powerful technique for robustness and well-suited to popular image compression standards. Frequency domain techniques are introduced such as DCT domain, DWT domain, DFT domain etc.

Discrete Cosine Transform (DCT) Watermarking Techniques

DCT works by separating images into parts of differing frequencies. Only the most important frequencies that remain are used to retrieve the image in the decompression process .The DCT works by separating images into different frequency bands. Thus, it is much easier to embed watermarking information into the middle frequency bands of an image. The middle bands are usually chosen to avoid the low frequency band that contained the most important parts of image. Moreover, the middle band is selected without exposes themselves to removal through compression and noise attacks in high frequency band [9]. Figure 2.7 shows watermarking technique based on DCT.

Figure 2.7 DCT domain watermarking

Discrete Fourier Transform (DFT) Watermarking Techniques

Fourier transform decomposes image function into a set of orthogonal functions and can transform the spatial intensity image into its frequency domain [9].

The main drawbacks of using FFT are it has lesser ability to withstand JPEG compression and cropping attack. Besides, the loss of time information in a signal by Fourier Transform will lead to the difficulty in processing.

Wavelet Transform Watermarking Techniques

The wavelet transform (WT) has gained well-known acceptance in signal processing and image compression. Inherent multi-resolution character makes wavelet-coding schemes suitable for applications where scalability and tolerable degradation are important. Sharon Shen [16]; the wavelet transform is computed separately for different segments of the time-domain signal at different frequencies. Meaning that, DWT analyzes the signal at different frequencies giving different resolutions. The multi-resolution analysis is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies.

In [15],[18], [19] and [24], Discrete Wavelet Transform (DWT) is proposed because it is closer to human visual system (HVS) as compared to Fourier Transform (FT) and Discrete Cosine Transform (DCT). This advantage of the DWT allows using higher energy watermarks in regions where the HVS is known to be less sensitive so that embedding watermarks in these regions provides to increase the robustness and imperceptibility of the watermarking techniques.

2.5 Literature research by previous researchers

Recently, many authentication watermarking schemes have been proposed in the literature [10-26], that focus on frequency transform-domain due to its excellent feature presentation. Besides, most of the watermarking researches are focused on images. The reason is a large demand for image watermarking products due to the fact that there are so many images available much more than audio and video at no cost on the World Wide Web, which need to be protected.

C.M. Kung et al. [12] presented DCT domain for a robust watermarking and image authentication. They hide the watermark in the middle frequency region which is selected according to the values of the table Q (u, v). The watermark technique were designed based on the fragile watermarking scheme such that when the watermark is under malicious falsification such as modification or alteration, the damaged image can be detected and positioned according to the conditions of damaged signature

Ameya Naik and Raghunath S. Holambe [13] used DCT for Biometric Authentication. The embedding process is based on changing the selected DCT coefficients of the host image to odd or even values depending on the binary bit value of watermark DCT coefficients. In this technique the face image was embedded into a fingerprint host image. The fifteen DCT coefficients of the logo are converted into bit strings using mapping technique. The bit was embedded into ten low frequency band coefficients of the DCT sub-blocks.

In [18], watermarking and image authentication technique using semi-fragile watermark based on DWT is proposed. The image was decomposed into wavelet coefficients, a visual recognizable logo and content based watermark information is embedded in the wavelet coefficients. The wavelet coefficients corresponding to the points located in a neighborhood that have maximum entropy are used for embedding the visual logo and the relations between the neighboring coefficients in the selected wavelet sub bands are embedded into middle frequency pairs of the first scale coefficients.

Yuan-Liang Tang and Ching-Ting Chen [19]; in their paper mentioned the use of semi-fragile watermarks for achieving better robustness based on DWT coefficients. At the embedding process, wavelet transformation is first applied on the host image. Then, for each of the 2-scale wavelet coefficient, its relation with its neighboring coefficient was computed and recorded, followed by adjusted the value of the later in order to make a stronger preservation of the relation. During authentication, the same feature extraction process is performed on the received image, which may have been tampered with. The watermarked image was extracted as well.

The problem of the DWT for some applications is that the coefficients in the decomposed subbands are real values, and then some modifications may result in loss of information and errors at the reconstruction stage. Consequently, the original image cannot be reconstructed from the watermarked image and perfect reconstruction cannot be achieved. In view of the above problems, Integer Wavelet Transform (IWT) was proposed to achieve perfect reconstruction of images. The IWT enables to reconstruct an integer signal perfectly from the computed integer coefficients that is suitable to apply in authentication and verification of digital content [21], [22], [23], [28] and [30].

M. A. El-Iskandarani et al. [22] presents a method which can add the watermark to the significant coefficients in Lifting Wavelet Transform (LWT) due to its good feature representation, fast computing speed and easy implementation. This wavelet transform provides very high computing speed, more memory-efficient and more suitable in lossless data compression applications.

Ru Guanying et al. [25] presented a new meaningful zero watermark algorithm based on combined spatial and time domain. They embeds the zero-watermark in the highest bit planes of spatial domain and wavelet domains' low frequency sub-image simultaneously, based on content's importance.

S. Kurshid Jinna et al. [28] proposed a distortionless image data hiding algorithm based on IWT that can hide watermark information into the original image. The data can be retrieved and the original image can be recovered without any distortion after extracted. This algorithm hides data into one or more middle bit-plane(s) of the IWT coefficients in the LH, HL and HH frequency sub bands.

Wavelet-domain Domain Technologies

In image processing and compression applications two-dimensional wavelet is applied because their inheritance of multi-resolution characteristic Therefore, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important [16] and [33].

Wavelet Decomposition

For 2D images, the wavelet transform is done in both horizontal and vertical directions. Firstly, applying 1D wavelet horizontally to each row of the image giving Low Horizontal (LH) and High Horizontal (HH) and then applying on all the column of the image giving (LL, LH) and (HL, HH) can be computed the 2D transform.

The LL sub band represents the coarse-scale DWT coefficients, while LH, HL and HH sub bands represent the fine-scale of DWT of DWT coefficients. The next coarse scale of wavelet coefficient can be determined by further process the LL sub band, until some final scale of N is reachable. When N is reached, 3N+1 sub band is obtained, consisting of the multi resolution subbands LL, LH, HL, and HH sub bands. Example of an image being decomposed into ten sub bands for three levels is shown in Figure 2.8.

Most of the image energy is concentrated at the lower frequency sub-bands LL and therefore embedding watermarks in these sub-bands may degrade the image significantly. However, embedding in the low frequency sub-bands could increase the robustness significantly. On the other hand, the high frequency sub-bands HH include the edges and textures of the image and the human eye is not generally sensitive to changes in such sub-bands. This allows the watermark to be embedded without being seen by the human eye. The best method is to embed the watermark in the middle frequency sub-bands LH and HL where acceptable performance of imperceptibility and robustness could be achieved [34].

Figure 2.8 DWT Decomposition of Image

Wavelet Reconstruction

From DWT coefficients as mentioned before, the original image can be reconstructed. The wavelet image reconstruction is similar to the inverse of the wavelet decomposition. The original image is obtained by concatenating all the coefficients, starting from the last level of decomposition. This process is continued through the same number of levels as in the decomposition process.

Possible Attacks on Image Watermarking

The attackers intending at the watermarked images can be classified as unintentional or intentional. The attackers have three strategies to defeat watermark robustness via:

To remove enough watermark signal

To jam the hidden communication channel

To desynchronize the watermarked content

The attacker's goals against Fragile Watermark on authentication applications are listed below [35]:

Make the watermark still valid after alteration of watermarked image

Generate a valid watermarked for new data

Different types of attacks will be described in order to test the robustness of watermarking schemes. The most popular classification is summarized in Figure 2.9 [9] and [35].

Figure 2.9 Attacks on Watermarks

2.8 Applications of Watermarking

There is a wide variety of applications in watermarking. Several applications are listed below.

Authentication

In authentication applications, the objective is to detect any modifications made on the cover work. This can be done using a fragile watermark. If the work is modified maliciously, the watermark will be destroyed. If the watermark can be retrieved at the recipient, the work is considered as authentic; if not it should be discarded as a fake. A low level of compression is usually permitted but not content alteration. Therefore a fragile watermark will have some degree of robustness. In the field of medical diagnosis, military and forensic evidences, authenticity of media content are important [3] and [5].

Copyright Protection

Digital watermarks are capable to be used in identifying and protecting the copyright ownership of the content. It also can be used in tracing illegally distribution copies [2], [3], [4] and [9].

Identity Card / Passport Security

In the field of data security, watermarks may be used for authentication, certification, and conditional access such as in identity card and passport security [9]. Information in a passport or ID card can also be included in the person's photo that appears on the ID card. The insertion of the watermark provides an extra level of security in this application. For example if ID card is stolen and he/she replaces the picture, the failure in extracting the watermark will invalidate the ID card [2], [9] and [36].

Tracking

Digital watermarks can be used to track the usage of digital content. Each copy of digital content can be uniquely watermarked with metadata specifying the authorized users of the content. Tracking application is used to detect illegal copying of content by identifying the users who fake the content illegally. The watermarking technique used for tracking is called as fingerprinting. Fingerprint is a real advance in identifying real manufactured objects from fake ones based on digital images of the original product stored in a protected server [2], [9] and [37].

Summary

Based on literature review, most of the researchers overcome the limitations of traditional watermarking technique with new approach to enhance the security levels in digital multimedia content. The theoretical concept helps to determine the techniques of proposed project. This project decided to apply several watermarking technique to provide image watermark authentication.