Data Hiding Schemes Based on Singular Value Decomposition

Introduction

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: