In the present-day era of typical social media communications, it is vital that privacy is to be preserved due to attacks on multimedia data through various methods. It is well known that the encryption standards either lack in keyspace or poor encryption strength. Hence, it is proposed to use a novel method in which both keyspace and encryption are increased.
Discrete cosine transforms (DCT) of the image are encrypted using a generalized logistic equation. Due to this idea, both compression and encryption are done simultaneously. Before applying DCT, the image is shuffled using Arnold Cat Map. The proposed compression and encryption method is validated using several chaotic metrics such as; Bifurcation diagram, Mutual information, Kolmogorov Sinai Entropy density, Kolmogorov Sinai Entropy generality, Space-Amplitude diagram, Space-Time diagram. Intruders are discouraged through the improved metrics such as NPCR (Number of Pixels changing rate), UACI (Unified Averaged changed intensity), and mutual information among the lattices used as the key.
In the same way, image reconstruction quality is improved and verified through the metrics such as PSNR (Peak Signal to noise ratio) and FOM (Figure of Merit). Compression performance is evaluated through the metric CR (Compression ratio). The performance is evaluated for both gray and color image databases under a noisy channel environment.
Table of Contents
CHAPTER 1: INTRODUCTION
1.1 Motivation
1.2 Encryption Basics
1.2.1 Terminologies in Image Encryption
1.2.2 Terminologies in Cryptography
1.3 Image Compression Basics
1.4 Challenges to Meet
1.5 Thesis Organization
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
2.2 Non-Chaotic Based Encryption
2.3 Chaotic Based Encryption
2.3.1 Full Encryption
2.3.1.1 Spatial Domain
2.3.1.2 Frequency Domain
2.3.1.3 Hybrid Domain
2.3.2 Selective Encryption
2.3.2.1 Spatial Domain
2.3.2.2 Frequency Domain
2.3.2.3 Hybrid Domain
2.4 Compression Methods
2.4.1 Lossy Compression Methods
2.4.1.1 JPEG – Joint Picture Expert Group
2.4.1.2 JPEG 2000
2.4.2 Lossless Compression Methods
2.5 Summary
CHAPTER 3: CHAOS THEORY IN ENCRYPTION
3.1 Summary
CHAPTER 4: MATERIALS AND METHODS
4.1 Introduction
4.2 Chaotic Metrics
4.2.1 Mutual Information
4.2.2 Kolmogorov-Sinai Entropy (Density And Generality)
4.2.3 Bifurcation Diagram
4.2.4 Standard Deviation of Lattice Value
4.3 Image Quality Metrics
4.4 Proposed Encryption
4.5 Compression Scheme
4.5.1 Discrete Wavelet Transforms
4.5.2 Wavelet Filter Banks
4.5.3 Properties of Wavelets
4.6 Summary
CHAPTER 5: RESULTS AND DISCUSSIONS
5.1 Mutual Information
5.2 KSE Density and Generality
5.3 Bifurcation Diagram
5.4 Space-Time Diagram
5.5 Space-Amplitude Diagram
5.6 Encryption Performance (Gray)
5.7 Key Sensitivity Analysis (Gray)
5.7.1 UACI and NPCR
5.7.2 Correlation Co-Efficient
5.8 Encryption Performance (Color)
5.9 Key Sensitivity Analysis (Color)
5.9.1 UACI and NPCR
5.9.2 Correlation Co-Efficient
5.10 Summary
CHAPTER 6: CONCLUSIONS, FUTURE SCOPES, AND APPLICATIONS
6.1 Future Scope
6.2 Applications
Research Objectives and Key Themes
This work aims to develop an improved image encryption and compression method by utilizing chaotic systems to overcome deficiencies in existing standards, specifically focusing on expanding keyspace and enhancing encryption strength. The primary research question addresses how to integrate chaotic metrics and optimized compression techniques to secure multimedia data in wireless environments effectively.
- Chaotic-based image encryption using generalized logistic maps.
- Sequential encryption and compression for optimized storage and security.
- Performance validation using chaotic metrics (e.g., Bifurcation diagram, Mutual information, KS Entropy).
- Application of Discrete Wavelet Transforms (DWT) for lossless compression.
Excerpt from the Book
1.1 MOTIVATION
As long as a communication network exists in the world, there would be hacking and stealing up of data. Apart from commercial loss due to such hacks, it would result in the collapse of the database, for example, an image database maintained in a forensic institution. It is also commonly seen in cyberspace that a popular website is hacked and data are stolen or spoiled. This would result in a high degree of uncertainty in the security of the data. It should be noted that there is a tremendous increase in the no of users on the internet day by day which eventually increases the amount of text data and multimedia data to be handled and hence storage capacity is proportionally increased as well. This is an abnormal scenario that exists at present, which is being compensated through available methods of encryption and compression standards. There exists a large gap between the expected image encryption standards and available methods. This has highly motivated me to conduct the research in a new orientation to a better extent and improve the metrics of the existing encryption standards and methods.
Summary of Chapters
CHAPTER 1: INTRODUCTION: This chapter provides the motivation for the research, highlighting the challenges of data security in modern digital communication and introducing fundamental concepts of encryption and compression.
CHAPTER 2: LITERATURE REVIEW: This chapter surveys existing research on non-chaotic and chaotic encryption methods, identifying key limitations in current security standards and compression techniques.
CHAPTER 3: CHAOS THEORY IN ENCRYPTION: This chapter explores the application of chaos theory, particularly using Arnold's cat map and logistic maps, for secure image transformation and pixel diffusion.
CHAPTER 4: MATERIALS AND METHODS: This chapter details the methodology of the proposed encryption and compression algorithms, defining the chaotic and image quality metrics used for performance evaluation.
CHAPTER 5: RESULTS AND DISCUSSIONS: This chapter presents the experimental findings and analysis of the proposed encryption method applied to both gray and color images, including key sensitivity and performance metrics.
CHAPTER 6: CONCLUSIONS, FUTURE SCOPES, AND APPLICATIONS: This chapter summarizes the research findings, suggests future extensions such as contourlet transformations, and discusses practical applications in medical and secure data transmission environments.
Keywords
ACGLML, Chaotic Systems, Space-Time Diagrams, KS Entropy Density, Bifurcation Diagram, Space-Amplitude Diagram, Discrete Cosine Transforms, Image Encryption, Image Compression, Keyspace, NPCR, UACI, DWT, MATLAB, Information Security.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on designing a novel, robust image encryption and compression method that addresses the limitations of existing chaotic-based encryption standards regarding keyspace and security strength.
What are the primary thematic areas explored?
The study centers on chaos theory, image transformation (scrambling), data diffusion using generalized logistic maps, and lossless compression using wavelet transforms.
What is the primary objective or research question?
The objective is to create a sequential encryption and compression scheme that provides infinite keyspace, improved chaotic metrics, and secure data transmission for multimedia and medical image databases.
Which scientific methods are utilized?
The methodology employs 2D Arnold cat map for permutation, generalized logistic equations for diffusion, and Discrete Wavelet Transforms (DWT) for compression. Validation is performed via chaotic metrics like Bifurcation diagrams, Mutual Information, and KS Entropy.
What is covered in the main body of the work?
The main body covers a comprehensive literature review, the theoretical framework of chaos theory in encryption, the proposal of the ACGLML method, and extensive results comparing the proposed system against established benchmarks.
Which keywords characterize the work?
Key terms include ACGLML, Chaotic Systems, Image Encryption, Data Compression, Keyspace, NPCR, UACI, and Space-Time/Amplitude analysis.
How does this method handle "periodic windows"?
The proposed ACGLML method utilizes a generalized logistic map which eliminates periodic windows found in traditional bifurcation diagrams, thereby maintaining chaos over a wider range of control parameters.
Why is the "Standard Deviation of Lattice Value" significant?
It is used as a new metric to quantify the turbulence in lattice values; a higher standard deviation indicates greater dynamic variation and thus better haze against unauthorized decryption attempts.
- Arbeit zitieren
- Dr. Pradheep Manisekaran (Autor:in), 2021, Image Encryption by Using ACGLML, München, GRIN Verlag, https://www.grin.com/document/1022972
