Digital Media Steganography: Principles, Algorithms, and Advances

The common use of the Internet and cloud services in transmission of large amounts of data over open networks and insecure channels, exposes that private and secret data to serious situations. Ensuring the information transmission over the Internet is safe and secure has become crucial, consequently information security has become one of the most important issues of human communities because of increased data transmission over social networks. Digital Media Steganography: Principles, Algorithms, and Advances covers fundamental theories and algorithms for practical design, while providing a comprehensive overview of the most advanced methodologies and modern techniques in the field of steganography. The topics covered present a collection of high-quality research works written in a simple manner by world-renowned leaders in the field dealing with specific research problems. It presents the state-of-the-art as well as the most recent trends in digital media steganography.

• Covers fundamental theories and algorithms for practical design which form the basis of modern digital media steganography
• Provides new theoretical breakthroughs and a number of modern techniques in steganography
• Presents the latest advances in digital media steganography such as using deep learning and artificial neural network as well as Quantum Steganography

• Language: English
• Publisher: Academic Press
• Release date: Jun 27, 2020
• ISBN: 9780128194393

Book preview

Digital Media Steganography

Principles, Algorithms, and Advances

First edition

Mahmoud Hassaballah

Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt

Table of Contents

Cover image

Title page

Copyright

List of contributors

About the editor

Preface

Acknowledgments

1: Introduction to digital image steganography

Abstract

1.1. Introduction

1.2. Applications of steganography

1.3. Challenges facing steganography

1.4. Steganographic approaches

1.5. Performance evaluation

1.6. Conclusion

References

2: A color image steganography method based on ADPVD and HOG techniques

Abstract

2.1. Introduction

2.2. Review of the ADPVD method

2.3. The pixel-based adaptive directional PVD steganography

2.4. Results and discussion

2.5. Conclusion

References

3: An improved method for high hiding capacity based on LSB and PVD

Abstract

3.1. Introduction

3.2. Related work

3.3. The proposed method

3.4. Results and discussion

3.5. Conclusion

References

4: An efficient image steganography method using multiobjective differential evolution

Abstract

4.1. Introduction

4.2. Literature review

4.3. Background

4.4. The proposed method

4.5. Experimental results

4.6. Conclusion

References

5: Image steganography using add-sub based QVD and side match

Abstract

5.1. Introduction

5.2. Proposed ASQVD+SM technique

5.3. Experimental analysis

5.4. Conclusion

References

6: A high-capacity invertible steganography method for stereo image

Abstract

Acknowledgement

6.1. Introduction

6.2. Preliminaries

6.3. The proposed method

6.4. Experimental results

6.5. Conclusion

References

7: An adaptive and clustering-based steganographic method: OSteg

Abstract

Acknowledgements

7.1. Introduction

7.2. Related works

7.3. OSteg embedding

7.4. Experimental results and discussion

7.5. Conclusion

References

8: A steganography method based on decomposition of the Catalan numbers

Abstract

8.1. Introduction

8.2. Related works

8.3. Decomposition of Catalan numbers

8.4. Implementation of the proposed method

8.5. Steganalysis and security testing

8.6. Conclusion

References

9: A steganography approach for hiding privacy in video surveillance systems

Abstract

9.1. Introduction

9.2. Related works

9.3. Hiding privacy information using video compression concept

9.4. Experimental results

Conclusion

References

10: Reversible steganography techniques: A survey

Abstract

Acknowledgements

10.1. Introduction

10.2. Difference Expansion (DE) schemes

10.3. Histogram-Shifting (HS) schemes

10.4. Pixel-Value-Ordering (PVO) schemes

10.5. Dual-image-based schemes

10.6. Interpolation-based schemes

10.7. Conclusion

References

11: Quantum steganography

Abstract

Acknowledgements

11.1. Introduction

11.2. Goals and tools of quantum steganography

11.3. Quantum steganography with depolarizing noise

11.4. Steganographic encoding in error syndromes

11.5. Encoding in the binary symmetric channel

11.6. Encoding in the 5-qubit perfect code

11.7. Secrecy and security

11.8. Asymptotic rates in the noiseless case

11.9. Asymptotic rates in the noisy case

11.10. Discussion and future directions

11.11. Conclusion

References

12: Digital media steganalysis

Abstract

12.1. Introduction

12.2. Image steganalysis

12.3. Audio steganalysis

12.4. Video steganalysis

12.5. Text steganalysis

12.6. Conclusion

References

13: Unsupervised steganographer identification via clustering and outlier detection

Abstract

Acknowledgement

13.1. Introduction

13.2. Primary concepts and techniques

13.3. General frameworks

13.4. Ensemble and dimensionality reduction

13.5. Conclusion

References

14: Deep learning in steganography and steganalysis

Abstract

14.1. Introduction

14.2. The building blocks of a deep neuronal network

14.3. The different networks used over the period 2015–2018

14.4. Steganography by deep learning

14.5. Conclusion

References

Index

Copyright

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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Editorial Project Manager: Gabriela D. Capille

Production Project Manager: Maria Bernard

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Typeset by VTeX

List of contributors

A.S. AbdelRady     South Valley University, Faculty of Science, Department of Mathematics, Qena, Egypt

Aditya Kumar Sahu

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Department of Computer Science and Engineering, GMRIT, Rajam, Andhra Pradesh, India

Adnan Hasanović     Department of Philological Sciences, University of Novi Pazar, Novi Pazar, Serbia

Ahmed Elhadad

Faculty of Science, South Valley University, Department of Mathematics and Computer Science, Qena, Egypt

Faculty of Science and Art, Jouf University, Department of Computer Science and Information, Al Qurayyat, Saudi Arabia

Alejandro Mora Rubio     Universidad Autónoma de Manizales, Department of Electronics and Automation, Manizales, Caldas, Colombia

Amal Khalifa     Purdue Fort Wayne University, Department of Computer Science, West Lafayette, IN, United States

Anita Pradhan     Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Dilbag Singh     Manipal University Jaipur, Computer Science and Engineering, School of Computing and Information Technology, Jaipur, India

Gandharba Swain     Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Gustavo Isaza     Universidad de Caldas, Department of Systems and Informatics, Manizales, Caldas, Colombia

Hanzhou Wu     School of Communication and Information Engineering, Shanghai University, Shanghai, China

Harold Brayan Arteaga Arteaga     Universidad Autónoma de Manizales, Department of Electronics and Automation, Manizales, Caldas, Colombia

Hussein Abulkasim     Faculty of Science, New Valley University, Department of Mathematics, El-Kharja, Egypt

Jesus Alejandro Alzate Grisales     Universidad Autónoma de Manizales, Department of Electronics and Automation, Manizales, Caldas, Colombia

K. Raja Sekhar     Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

M. Hassaballah     South Valley University, Faculty of Computers and Information, Department of Computer Science, Qena, Egypt

Manjit Kaur     Manipal University Jaipur, Computer Communication and Engineering, School of Computing and Information Technology, Jaipur, India

Marc Chaumont

Montpellier University, LIRMM (UMR5506)/CNRS, Nîmes University, Montpellier, France

LIRMM/ICAR, Montpellier, France

Mario Alejandro Bravo Ortíz     Universidad Autónoma de Manizales, Department of Electronics and Automation, Manizales, Caldas, Colombia

Marwa Saidi     Laboratoire RISC, Ecole Nationale d'Ingénieurs de Tunis, University of Tunis El Manar, Tunis, Tunisia

Mohamed Abdel Hameed     Luxor University, Faculty of Computers and Information, Department of Computer Science, Luxor, Egypt

Monagi H. Alkinani     University of Jeddah, College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, Jeddah, Saudi Arabia

Muzafer Saračević     Department of Computer Sciences, University of Novi Pazar, Novi Pazar, Serbia

Olfa Mannai     Laboratoire RISC, Ecole Nationale d'Ingénieurs de Tunis, University of Tunis El Manar, Tunis, Tunisia

Phuoc-Hung Vo

School of Engineering and Technology, Tra Vinh University, Tra Vinh City, Tra Vinh Province, Vietnam

College of Information Technology, Can Tho University, Can Tho, Vietnam

Rasheed Hussain     Institute of Information Systems, University of Innopolis, Tatarstan, Russia

Raúl Ramos-Pollán     Universidad de Antioquia, Department of Systems Engineering, Medellín, Antioquia, Colombia

Reinel Tabares-Soto     Universidad Autónoma de Manizales, Department of Electronics and Automation, Manizales, Caldas, Colombia

Rhouma Rhouma

Laboratoire RISC, Ecole Nationale d'Ingénieurs de Tunis, University of Tunis El Manar, Tunis, Tunisia

College of Applied Sciences, Salalah, Sultanate of Oman

Safwat Hamad     Faculty of Computer and Information Sciences, Ain Shams University, Department of Scientific Computing, Cairo, Egypt

Saleh Aly     Aswan University, Faculty of Engineering, Department of Electrical Engineering, Aswan, Egypt

Samed Jukić     Faculty of Information Tech., International Burch University, Ilidža, Sarajevo, BIH

Simon Orozco-Arias

Universidad Autónoma de Manizales, Department of Computer Science, Manizales, Caldas, Colombia

Universidad de Caldas, Department of Systems and Informatics, Manizales, Caldas, Colombia

Thai-Son Nguyen     School of Engineering and Technology, Tra Vinh University, Tra Vinh City, Tra Vinh Province, Vietnam

Thanh Nhan Vo     Chaoyang University of Technology, Department of Information Management, Taichung, Taiwan, R.O.C.

Thanh-Nghi Do     College of Information Technology, Can Tho University, Can Tho, Vietnam

Todd A. Brun     University of Southern California, Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, CA, United States

Tzu-Chuen Lu     Chaoyang University of Technology, Department of Information Management, Taichung, Taiwan, R.O.C.

Van-Thanh Huynh     School of Engineering and Technology, Tra Vinh University, Tra Vinh City, Tra Vinh Province, Vietnam

Vijay Kumar     NIT Hamirpur, Computer Science and Engineering, Hamirpur, HP, India

About the editor

Mahmoud Hassaballah was born in 1974, Qena, Egypt. He received his B.Sc. degree in mathematics in 1997 and his M.Sc. degree in computer science in 2003, both from South Valley University, Egypt, and his Doctor of Engineering (D. Eng.) in computer science from Ehime University, Japan, in 2011. He was a visiting scholar with the Department of Computer & Communication Science, Wakayama University, Japan, in 2013 and GREAH laboratory, Le Havre Normandie University, France, in 2019. He is currently an associate professor of computer science at the Faculty of Computers and Information, South Valley University, Egypt. He served as a reviewer for several Journals such as IEEE Transactions on Image Processing, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Parallel and Distributed Systems, Pattern Recognition, Pattern Recognition Letters, Egyptian Informatics Journal, IET Image Processing, IET Computer Vision, IET Biometrics, Journal of Real-Time Image Processing, The Computer Journal, Journal of Electronic Imaging, and Optical Engineering. He has published 5 books and over 50 research papers in refereed international journals and conferences. His research interests include feature extraction, object detection/recognition, artificial intelligence, biometrics, image processing, computer vision, machine learning, and data hiding.

Preface

Mahmoud Hassaballah     Department of Computer Science Faculty of Computers and Information South Valley University, Qena, Egypt

In the information age and digital communication revolution the Internet and cloud services are widely used in transmission of large amounts of data over social media like Facebook, Instagram, WhatsApp, and several other insecure networks, exposing private and secret data to serious situations. Also, novel technologies and new applications such as internet of things and artificial intelligence bring new threats. Consequently, ensuring that information transmission over these mediums is safe and secure has become one of the most important issues in the field of data security. To keep unauthorized persons away from the transmitted information, a variety of techniques have been introduced, and steganography is one of them. Steganography as a technique for covert communication aims to hide secret messages in a normal message achieving the least possible statistical detectability and without drawing suspicion during data communication. Steganography differs from other data security techniques (cryptography and watermarking). For example, cryptography conceals only the content of the message through encryption, whereas steganography conceals the presence of the message itself. In a broader sense, watermarking, as an old data security technique, and steganography share some common features with each other, but fundamentally they are quite different. Cryptography and watermarking are beyond the scope of the present work.

The purpose of this book is to provide researchers, scholars, postgraduate students, possibly senior undergraduate students who are taking an advanced course in related topics, and professionals with the foundations, basic principles, and technical information regarding digital media steganography (i.e., image, video, text, and audio data). Besides, it is intended to be a comprehensive reference volume and to provide a bird's eye view of recent state-of-the-art methods on the topic of steganography. Further, several new methods are presented in this book such as quantum steganography. The emergence of deep learning in steganography and steganalysis is also discussed. The book consists of fourteen high-quality chapters written by renowned experts in the field. Each chapter provides principles and fundamentals of a specific method, introduces reviews up-to-date techniques, and presents outcomes. In each chapter, figures, tables, and sometimes examples are used to improve presentation and analysis of the proposed method. Furthermore, bibliographic references are included in each chapter, providing a good starting point for deeper research and further exploration of the book topics. The book is structured so that each chapter is self-contained within reasonable limits and can be read independently from the others as follows.

Chapter 1: Introduction to digital image steganography presents a general and brief introduction to concepts, applications, challenges, and methods of image steganography. It also provides short descriptions of several metrics used in performance evaluation of steganography methods.

Chapter 2: A color image steganography method based on ADPVD and HOG techniques introduces a new pixel-based adaptive directional pixel value differencing (P-ADPVD) data hiding method consisting of five algorithms. The main advantage of the P-ADPVD method is embedding secret data in three different edge directions, rather than only in one direction as in the PVD-based methods. This methodology significantly improves the quality and security of stego images without sacrificing the embedding capacity.

Chapter 3: An improved method for high hiding capacity based on LSB and PVD introduces an image steganography method using the concept of least significant bit substitution and pixel value differencing. The major contributions of the method are: increasing hiding capacity, avoiding fall off boundary problem, and resistance to regular and singular attack. It achieves high hiding capacity having 800007 bits with maintaining good PSNR at 36.03 dB and shows great resistance to attacks.

Chapter 4: An efficient image steganography method using multiobjective differential evolution proposes a steganography method based on differential evolution and least significant substitution (LSB) scheme. Differential evolution is utilized to optimize the mask assignment list of the LSB scheme. Several evaluation results and comparisons are reported.

Chapter 5: Image steganography using add-sub based QVD and side match is a new steganography method to address the FOBP. It performs two stages of embedding on 3×3 size disjoint pixel blocks. In the first stage, it performs ASQVD and remainder substitution on central pixel and its left, right, lower, and upper neighbor pixels. Based on new values of these five pixels, in the second stage, it performs SM embedding approach on four corner neighbors. The average hiding capacity achieved by this method was 3.55 bpp.

Chapter 6: A high-capacity invertible steganography method for stereo image proposes a new invertible steganography method for high embedding capacity using two-dimensional histogram shifting in transform domain. It investigates DCT-quantized coefficients of each similar block pair in the left and right views of stereo image. Then secret bits are first partitioned into 3-bit groups and encoded into decimal form. The embedding direction histogram is built and used for embedding. This method achieves the trade-off between imperceptibility and embedding capacity.

Chapter 7: An adaptive and clustering-based steganographic method: OSteg proposes a new adaptive spatial steganographic method based on Otsu clustering technique. It uses clustering pixels values to restrict the embedding alteration to rich-textured blocks only. The security of the method is enhanced through exploiting a nonlinear Ikeda system. The embedding of a secret message is carried out in preprocessing and alteration phases.

Chapter 8: A steganography method based on decomposition of the Catalan numbers discusses some mathematical concepts of number theory and applications of combinatorial mathematics in the area of steganography. Then it has proposed a steganography method based on decomposition of the Catalan numbers.

Chapter 9: A steganography approach for hiding privacy in video surveillance systems presents a method for embedding video captured by a surveillance camera into another processed video form with removed private information. The hiding process is carried out in the discrete cosine transform domain of the cover video based on the H.264 video compression concept. Experimental results showed that this method achieved low distortion in the stego video while maintaining an acceptable visual quality for the retrieved frames.

Chapter 10: Reversible steganography techniques: A survey presents a detailed survey for the state-of-the-art reversible steganography techniques to provide for new researchers a concise introduction to the reversible steganography field.

Chapter 11: Quantum steganography inspects in detail quantum steganography protocols, describes different encoding methods, and proves bounds on the asymptotic rate of quantum steganographic communication and the rate of secret key consumption. Then it examines one particularly well-developed approach, in which the messages are disguised as errors on a quantum error-correcting code from a noisy quantum channel.

Chapter 12: Digital media steganalysis is devoted to recent trends in digital media steganalysis. Several steganalysis methods are discussed. It also identifies several opportunities as future research directions. It is a good starting point for research projects on digital media steganalysis, as useful methods can be isolated, and past errors can be avoided.

Chapter 13: Unsupervised steganographer identification via clustering and outlier detection presents concepts and advanced methodologies in the steganographer identification problem (SIP), where one or multiple users called actors are guilty of using steganography, and the goal is to identify the user who sends many steganographic images among other innocent users. It is self-contained and intended as a tutorial introducing the SIP in the context of digital media steganography.

Chapter 14: Deep learning in steganography and steganalysis deals with deep learning in steganalysis. It presents the structure of deep neural networks in a generic way, discuses the networks proposed in the literature for different scenarios of steganalysis, and describes steganography using deep learning. Some promising future lines of research are also introduced.

Finally, it is very necessary to mention here that the book is a small piece in the puzzles of data security and digital media steganography. We hope that readers will find the presented chapters in the book interesting and that the chapters will inspire future research from both theoretical and practical viewpoints to spur further advances in the field of data security.

March 3, 2020

Acknowledgments

Mahmoud Hassaballah     Department of Computer Science Faculty of Computers and Information South Valley University, Qena, Egypt

The editor would like to take this opportunity to express his sincere gratitude to the contributors for extending their wholehearted support in sharing some of their latest results and findings. Without their contributions, it would be not possible for the book to successfully come into existence. The reviewers of chapters deserve special thanks for their constructive and timely input. Finally, the editor is deeply grateful for the dedicated and professional work of the staff at ELSEVIER and for giving him the opportunity to edit a book on digital media steganography. In particular, I would like to thank Elizabeth Brown, senior acquisitions editor, and Gabriela Capille, editorial project manager, for initiating this project and for their kind and timely support in publishing the book and for handling the publication. The editorial staff at ELSEVIER has done a meticulous job, and working with them was a pleasant experience, with special thanks to Maria Bernard (Project Manager).

March 3, 2020

1

Introduction to digital image steganography

M. Hassaballaha; Mohamed Abdel Hameedb; Monagi H. Alkinanic    aSouth Valley University, Faculty of Computers and Information, Department of Computer Science, Qena, Egypt

bLuxor University, Faculty of Computers and Information, Department of Computer Science, Luxor, Egypt

cUniversity of Jeddah, College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, Jeddah, Saudi Arabia

Abstract

The Internet and cloud services are widely used in transmission of large amounts of data over open networks and insecure channels, exposing private and secret data to serious situations. Thus ensuring that information transmission over these mediums is safe and secure has become one of the most important issues in the data security field. To keep unauthorized persons away from the transmitted information, a variety of techniques have been introduced, and steganography is one of them. As a technique for covert communication, steganography aims to hide secret messages in a normal message achieving the least possible statistical detectability and without drawing suspicion. Steganography techniques can be applied on a various types of data such as text, image, video, and audio data. This chapter presents a general and brief introduction to exemplify the tremendous progress achieved recently in the topic of image steganography to help readers and researchers in discovering new research gaps in the theoretical foundations and practical implementations of steganography techniques.

Keywords

Privacy; Data security; Digital media steganography; Image steganography; Audio Steganography; Steganalysis

1.1 Introduction

Steganography has been used from ages and has been set its roots from ancient civilizations (e.g., Greece, Egypt). The word steganography is a composite of two Greek words: Steganos which means covered and Graphia which means writing. In the 5th century BC, Histaiacus shaved a slave's head and tattooed a message on his skull, and the slave was dispatched with the message after his hair grew back [1]. Cardan (1501–1576) reinvented a Chinese ancient method of secret writing, where a paper mask with holes is shared among two parties, this mask is placed over a blank paper, and a sender writes the secret message through the holes, then takes the mask off, and fills the blanks to display the message as an innocuous text. This method was approved to Cardan Grille [2]. Null Ciphers, Microdots, and invisible ink methods were also very popular steganographic methods during World War II. These hiding secret message methods have been used in various forms for thousands of years [3].

Nowadays, digital steganography can be defined as the art of hiding secret messages behind the innocent looking digital media [4]. Jessica [2] defined steganography as the art of concealed communication where the existence of a message is secret. Other researchers define digital steganography as a task of hiding digital information in covert channels so that one can conceal some information and prevent detection of this hidden information [5]. Steganography can be defined as a science of obscuring a message in a carrier (host object) with the intent of not drawing suspicion to the context in which the messages is transferred [6]. In general, there are several types of digital mediums, which can be used for hiding secret information such as image, video, audio, and text (linguistics) as shown in Fig. 1.1. These digital mediums have different characteristics to embed secret information [7], where the best medium for embedding secret information must have two features: the medium should be popular, and the modification in this host (cover) should be invisible to any unauthorized third party.

Figure 1.1 Categories of digital media steganography.

To the best of our knowledge, in the literature of digital steganography an image is the most popular medium that attracted steganographers [8]. Reasons for this popularity are the abundance of digital images on the Internet, the image provides enough redundancy to manipulate steganography, and Human Visual System HVS attributes motivate researchers to exploit these attributes in data hiding systems. Though images are popular in steganography, other media such as text is also another choice for performing steganography [9]. There are several data hiding applications related to text. For instance, in the context of copy-right protection, text watermarking may be used, whereas steganography can be employed for adding a hash to a text file for protecting from tampering. Unfortunately, the lack of redundancy in the texts compared to digital images makes steganography using texts a nontrivial challenge [10–13].

Fig. 1.2 shows the general pipeline of image steganography approaches, where the term cover image denotes the image that is used to embed the secret message [14,15]. Normally, any image steganography approach is composed of two algorithms, one for embedding, which is actually the procedure or algorithm that is used to hide the secret message within the cover image, and the other, extraction algorithm, which simply can be used to recover the secret message from the stego image [16]. Thus the stego image, as the final output image, embeds the secret information [17].

Figure 1.2 A general image steganography pipeline.

1.2 Applications of steganography

Simply, steganography can be employed anytime one wants to conceal some data. There are many reasons to hide data, but they all come into the desire to prevent unauthorized individuals from reaching the data or from becoming aware of the existence of a message. Steganography can be used quite effectively in the automatic monitoring of a radio advertisement or music. An automated system can be set up to watch for a specific stego message [3].

Modern computer and networking technology allows individuals of the basic applications of steganography related to secret communications. Clearly, groups and companies can be access to host a web page that may contain secret information meant for another party. Anyone can download the web page; however, the hidden information is invisible and does not take any attention [18]. Some modern applications of steganography are used in medical imaging systems [19], where a separation is considered necessary for confidentiality between patients' image data or DNA sequences and their captions such as Physician, Patient's name, address, and other particulars. Using steganography may help to avoid the leakage of patients' private data in unauthorized hands.

Inspired by the notion that steganography can be embedded as part of the normal printing process, the Japanese firm Fujitsu is going to develop technology for encoding data in a printed picture that is invisible to the human eye and later can be retrieved by a mobile phone with a camera [20]. The process takes less than one second as the embedded data is mere 12 bytes. Hence users are able to use their cell phones to capture encoded data. The basic idea is transforming the image color scheme before printing to its hue, saturation, and value components (HSV), then embedding it into the Hue domain to which human eyes are not sensitive. Mobile cameras can see and decode the coded data [21]. There are several other applications, which can use steganography to keep their communications secret [3], including:

•  Intelligence services or Intellectual Properties.

•  Securing multimodal biometric data.

•  Corporations with trade secrets to protect.

•  Governments claimed that criminals can use steganography to communicate. So, it may become limited under laws.

•  Military and defence communication.

In the business world, steganography can be used to hide a secret chemical formula or plan for new inventions. It can also be used for corporate espionage by sending out trade secrets. Also, it can be used in the noncommercial sector to hide information that someone wants to keep private.

1.3 Challenges facing steganography

In steganography techniques the statistics of the cover image are used to embed the secret information into it without changing its properties [14]. The resulting image is called a stego image. The stego image must be free from observable change, so that any third party cannot be able to discover these changes and handle this cover as a normal image, whereas the secret data transmitted through this image remain secure. Any image steganographic system faces the following major challenges shown in Fig. 1.3:

•  Size of payload: how a maximum embedding capacity can be achieved? Steganography aims sufficient embedding capacity. Requirements for higher payload and secure communication are often contradictory [22,23].

•  Visual image quality: how much the stego image is perceptually identical to its cover image? So the image steganography techniques should produce a high imperceptible stego image [24].

•  Robustness: how can a stego image resist the different steganalysis detection attacks? The stego image should provide robustness against image processing techniques like compression, cropping, resizing, and so on; that is, when any of these steganalysis techniques are performed on stego image, secret information should not be completely destroyed [25].

Figure 1.3 Trade-off between capacity, imperceptibility, and robustness.

Therefore the ideal steganographic method must fulfill the above objectives simultaneously as high capacity, good visual image quality, and undetectability. But most often, high payload steganographic approaches introduce the distortion artifacts in stego images that are vulnerable to steganalysis. The steganographic methods having good visual image quality suffer from the low payload. Thus, how to achieve simultaneously high payload, good visual quality, and undetectability is a real challenging research issue due to the contradictions between them [17].

1.4 Steganographic approaches

In this section, we attempt to give an overview of the most important steganographic approaches, which use digital images as a cover media by addressing the classification of image steganography. Based on the nature of embedding, there are various steganographic techniques available under image cover media containing spatial, transform spread spectrum and adaptive domains. In the spread spectrum the secret data is multiplied by a pseudonoise (PN) sequence and then modulated before embedding in the cover object. Spatial or image domain techniques use bitwise methods that apply bit insertion and noise manipulation using simple mechanisms, whereas the transform domain is defined as the transformation of an image into its frequency representation followed by modification on the spectral components of the image. Adaptive nature can be introduced in the data embedding schemes in several ways such as selecting the target pixels of the cover image, nature of modification to be made, and the number of bits embedded in a pixel [4]. Classification of image steganography techniques is illustrated in Fig. 1.1.

1.4.1 Spread spectrum approaches

These systems hide and recover a message of substantial length within digital imagery while maintaining the original image size and dynamic range. The embedded secret message can be recovered using appropriate keys without any knowledge of the original image. Image restoration, error-control coding, and techniques similar to the spread spectrum are illustrated. A message embedded by this method can be in the form of text, image, or any other digital signal. Applications for such schemes include in-band captioning, covert communication, image tamper-proofing, authentication, embedded control, and revision tracking [26].

1.4.2 Spatial domain approaches

Spatial domain schemes are more adapted with the human visual system (HVS) and can provide more embedding capacity than transform domain schemes with an acceptable image quality [27]. It is the simplest way of data embedding in digital images in which pixel values can be modified directly to encode the secret message bits. The main steganographic schemes coming under the spatial domain technique include Pixel Value Differencing (PVD), Least Significant Bit substitution (LSB), Exploiting Modification Direction (EMD), Quantization based, Gray Level modification, Multiple Bit-planes, and Palette-based steganography schemes [28–39].

1.4.2.1 Gray level modification

The gray level values of the pixels are checked and contrasted to the bit stream that is to be mapped in the image. First, the gray level values of the chosen pixels (odd pixels) are made even by changing the gray level by one unit. When all the chosen pixels have an even gray level, it is contrasted to the bit stream that must be mapped. The principal bit from the bit stream is contrasted to the initially chosen pixel. When the primary bit is even, the primary pixel is not altered as all the chosen pixels have an even gray level value. Whenever the bit is odd, the gray level value of the pixel is decremented by one unit to make its esteem odd, which then would represent an odd bit mapping. This is done for all bits in the bit stream, and every single bit is mapped by changing the gray level values consequently. These methods help us to provide better quality stego images compared to other methods [40].

1.4.2.2 Pixel value differencing (PVD)

This technique subdivides the cover image into nonoverlapping blocks consisting of two connecting pixels. It hides the data by altering the difference between these two pixels. The area of the pixel decides the hiding capacity of this technique. For example, if the edge area is chosen, then the difference is high in between the connected pixels, whereas in smooth areas the difference is low. Thus the best choice is to select edge areas to embed the secret message that is having more embedding capacity [29] as shown in Fig. 1.4.

Figure 1.4 Steps of the pixel value differencing method.

1.4.2.3 Least significant bit substitution (LSB)

Least significant bit (LSB) steganography is one of the fundamental and conventional methods that are capable of hiding large secret information in a cover image [28]. This technique is involved to replace all LSB bits of pixels within the cover image with secret bits. This method embeds the fixed-length secret bits in the same fixed length LSBs of pixels as shown in Fig. 1.5. Although this technique is simple, it generally causes noticeable distortion when the number of embedded bits for each pixel exceeds four [41,42].

Figure 1.5 Hiding data in images using LSB method.

1.4.2.4 Exploiting modification direction (EMD)

Exploiting modification direction (EMD) uses n array notational system to lessen the stego image distortion. Furthermore, embedding needs a decrease or increase from a specific pixel value within the set. For this method, it is necessary to compute for the value of n before embedding. The highest image quality is achieved when the value of n is equal to 2, where the embedding is represented by only one secret digit within each two pixels [43].

1.4.2.5 Quantization-based approaches

The steganographic system of this category uses any sort of encoding system to hide secret data bits. The encoding system is any standard compression codec like JPEG, vector quantization, and so on. The secret data are divided into small pieces of data, and these small data pieces are embedded along with the encoded carrier image. These systems are used for enhancing the capacity while minimizing the distortion of the stego image. Unfortunately, these systems are not sufficient to handle the geometrical attacks and steganalysis [4].

1.4.2.6 Multiple bit-planes-based approaches

These methods are introduced as an extension to the LSB substitution method, where bit planes are utilized for hiding secret data bits [44]. Usually, bit plane stego approaches are used along with other methods to boost the performance of the overall system. The expanded bit-plane encoding brings two advantages: it can host more secret bits than the 8-bit LSB techniques, and the degree of randomness of embedding is high [45].

1.4.3 Adaptive-based approaches

Adaptive steganography is known as Statistics-aware embedding [1] or Masking [46]. In other words, the statistics of the cover image are used to embed secret information without changing its properties. This embedding can be done by a random adaptive selection of pixels according to the cover image and the selection of pixels in a block with large local STD (Standard Deviation) [47]. The pixels that carry secret bits are selected adaptively depending on the content of the cover image [48].

1.4.4 Transform domain approaches

Several transform domain methods are utilized in the field of steganography and the most popular schemes include: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), Integer Wavelet Transform (IWT), and Complex wavelet transform (CWT) [49–51]. Basically, this type of technique is more robust with regard to common image processing operations and lossy compression. The DCT-based steganography is conveniently applied in the Joint Photographic Experts Group (JPEG) compression standards, whereas the DWT-based steganography conveniently applied in the Joint Photographic Experts Group 2000 (JPEG2000) compression standards. A block diagram of hiding data using the DWT-based methods is shown in Fig. 1.6.

Figure 1.6 Block diagram of hiding data using DWT.

1.5 Performance evaluation

1.5.1 Payload capacity

In fact, the embedding capacity (or playload capacity) depends on a steganography scheme and nature of the selected cover image. The capacity can be defined as the maximum size of secret data that can be embedded in the cover image without damaging the integrity of the cover image. In other words, the capacity is the number of bits embedded in each pixel and is represented by bits per pixel (bpp) or in relative percentage as follows:

(1.1)

1.5.2 Visual stego image quality analysis

There are several measures for assessment of stego image quality, and any measure can be used [52–54]. The most common measures used for comparing stego S and cover C are:

•  Peak-signal-to-noise ratio (PSNR) can be defined as a statistical image quality estimation used for measuring the