Data Hiding Schemes Based on Singular Value Decomposition

Data Hiding Schemes Based on Singular Value Decomposition

Nidhal Khdhair El Abbadi
DOI: 10.4018/978-1-4666-6583-5.ch012
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The security of information exchange is very important on the network. Authentication and information hiding have also become important issues. Information hiding techniques are acquiring an increasing importance due to the widespread diffusion of multimedia contents. The aim of this chapter is to focus on the Singular Value Decomposition (SVD) transform, with the aim of providing an exhaustive overview on those steganography, image cryptography and watermarking techniques leveraging on the important properties of such a transform. Despite the attention it has received in the last years, SVD in image processing and security is still in its infancy. Many SVD characteristics are still unutilized in image processing. In this chapter the author tries to highlight the basic properties of SVD and some of their applications in the field of security to encourage researchers to discover more about SVD properties which are not yet utilized.
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Due to the rising dependence on digital media and the unexpected expansion of the distribution opportunities over the Internet, techniques for hiding information into digital contents are achieving significant importance. Such techniques aim to provide the ability to communicate secretly and the capacity to protect copyrighted multimedia content against illegal distribution. Designing such schemes has become a topic of great importance and many researchers have spent much effort in the last years to obtain an effective solution. However, despite many different approaches have been attempted, there is currently no scheme that can preserve imperceptibility of the hidden data while ensuring a high security against malicious attacks.

In networked environments, the safety of multimedia data can be investigated according to two aspects: the safety of static data and the data security during dynamic communication. The safety of static multimedia data can be inspected according to the following four aspects:

  • 1.

    Storage: Is the data centrally stored, or dispersed?

  • 2.

    Vulnerability: How robustness is the data against theft or abuse?

  • 3.

    Confidence/Authenticity: What constitutes authentic information? Can that information be tampered with?

  • 4.

    Linking: Will the multimedia data be linked to other information, e.g., about originating and/or consuming party?

When inspecting the security of real-time multimedia communication, one should take into account the specific properties of both multimedia data and real-time communication. First, limited distortions in multimedia data cannot be perceived by end users. Thus some bit errors and packets loss that may occur during communication do not defect the overall visual/audio quality. Secondly, due to scheduling protocols of real-time multimedia communication, packet loss may happen. Thirdly, caused by the large amount of multimedia data, communication security trade-offs should be low enough.

The upcoming information processing architectures for ubiquitous computing is highly sensitive to security issues. For some networked scenarios, such as fingerprint collection in distributed environment, video monitoring and health care systems, the image integrity and authenticity is fatal to the success of these services. While most of the embedded systems working in such distributed environments are low-end devices in terms of their computing power, memory size and communication bandwidths. Therefore, new security policies have to provide, such that the given constraints of these devices are considered accordingly.

For the current digital age, digital forensic research becomes imperative. Counterfeiting and falsifying digital data or digital evidence with the goal of making illegal profits or bypassing laws is the main objective for the attackers. The forensic research focuses in many tracks; steganography, watermarking, authentication, labeling, captioning, etc. Many applications were developed to satisfy consumer requirements such as labeling, fingerprinting, authentication, copy control for DVD, hardware/ software watermarking, executables watermarks, and signaling (signal information for automatic counting) for the purpose of broadcast monitoring count (Sadek, 2012).

The SVD packs the maximum signal energy into few coefficients. It has the ability to adapt to the variations in local statistics of an image. However, SVD is an image adaptive transform; the transform itself needs to be represented in order to recover the data. Despite the attention it has received in the last years, SVD in image processing is still in its infancy. Many SVD characteristics are still unutilized in image processing. The present chapter highlights the basic properties of SVD and some of their applications in the field of security to encourage researchers to discover more about SVD properties which are not yet utilized (Liu & Tan, 2002).

Following some objectives of writing this chapter are:

Key Terms in this Chapter

Security: The field covers all the processes and mechanisms by which computer-based equipment, information and services are protected from unintended or unauthorized access, change or destruction.

Watermarking: The process of hiding digital information in a carrier signal; the hidden information should, but does not need to contain a relation to the carrier signal. Digital watermarks may be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners. It is prominently used for tracing copyright infringements and for banknote authentication.

SVD: The singular value decomposition: is a factorization of a real or complexmatrix, with many useful applications in signal processing and statistics.

Encryption: The process of encoding messages (or information) in such a way that third parties cannot read it, but only authorized parties can. Encryption doesn't prevent hacking but it prevents the hacker from reading the data that is encrypted.

Image Processing: Any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image.

Steganalysis: The art and science of detecting messages hidden using steganography; this is analogous to cryptanalysis applied to cryptography.

Information Hiding: The process of embedding information into digital content without causing perceptual degradation.

Steganography: The art and science of encoding hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message. It is a form of security through obscurity.

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