Multimedia Security, Volume 1: Authentication and Data Hiding

✍ Scribed by William Puech

• Publisher: Wiley-ISTE
• Year: 2022
• Tongue: English
• Leaves: 317
• Series: IMAGE: Compression, Coding and Protection of Images and Videos
• Category: Library

✦ Synopsis

Today, more than 80% of the data transmitted over networks and archived on our computers, tablets, cell phones or clouds is multimedia data - images, videos, audio, 3D data. The applications of this data range from video games to healthcare, and include computer-aided design, video surveillance and biometrics.

It is becoming increasingly urgent to secure this data, not only during transmission and archiving, but also during its retrieval and use. Indeed, in today’s "all-digital" world, it is becoming ever-easier to copy data, view it unrightfully, steal it or falsify it.

Multimedia Security 1 analyzes the issues of the authentication of multimedia data, code and the embedding of hidden data, both from the point of view of defense and attack. Regarding the embedding of hidden data, it also covers invisibility, color, tracing and 3D data, as well as the detection of hidden messages in an image by steganalysis.

✦ Table of Contents

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Foreword by Gildas Avoine
Foreword by Cédric Richard
Preface
1. How to Reconstruct the History of a Digital Image, and of Its Alterations
1.1. Introduction
1.1.1. General context
1.1.2. Criminal background
1.1.3. Issues for law enforcement
1.1.4. Current methods and tools of law enforcement
1.1.5. Outline of this chapter
1.2. Describing the image processing chain
1.2.1. Raw image acquisition
1.2.2. Demosaicing
1.2.3. Color correction
1.2.4. JPEG compression
1.3. Traces left on noise by image manipulation
1.3.1. Non-parametric estimation of noise in images
1.3.2. Transformation of noise in the processing chain
1.3.3. Forgery detection through noise analysis
1.4. Demosaicing and its traces
1.4.1. Forgery detection through demosaicing analysis
1.4.2. Detecting the position of the Bayer matrix
1.4.3. Limits of detection demosaicing
1.5. JPEG compression, its traces and the detection of its alterations
1.5.1. The JPEG compression algorithm
1.5.2. Grid detection
1.5.3. Detecting the quantization matrix
1.5.4. Beyond indicators, making decisions with a statistical model
1.6. Internal similarities and manipulations
1.7. Direct detection of image manipulation
1.8. Conclusion
1.9. References
2. Deep Neural Network Attacks and Defense: The Case of Image Classification
2.1. Introduction
2.1.1. A bit of history and vocabulary
2.1.2. Machine learning
2.1.3. The classification of images by deep neural networks
2.1.4. Deep Dreams
2.2. Adversarial images: definition
2.3. Attacks: making adversarial images
2.3.1. About white box
2.3.2. Black or gray box
2.4. Defenses
2.4.1. Reactive defenses
2.4.2. Proactive defenses
2.4.3. Obfuscation technique
2.4.4. Defenses: conclusion
2.5. Conclusion
2.6. References
3. Codes and Watermarks
3.1. Introduction
3.2. Study framework: robust watermarking
3.3. Index modulation
3.3.1. LQIM: insertion
3.3.2. LQIM: detection
3.4. Error-correcting codes approach
3.4.1. Generalities
3.4.2. Codes by concatenation
3.4.3. Hamming codes
3.4.4. BCH codes
3.4.5. RS codes
3.5. Contradictory objectives of watermarking: the impact of codes
3.6. Latest developments in the use of correction codes for watermarking
3.7. Illustration of the influence of the type of code, according to the attacks
3.7.1. JPEG compression
3.7.2. Additive Gaussian noise
3.7.3. Saturation
3.8. Using the rank metric
3.8.1. Rank metric correcting codes
3.8.2. Code by rank metric: a robust watermarking method for image cropping
3.9. Conclusion
3.10. References
4. Invisibility
4.1. Introduction
4.2. Color watermarking: an approach history?
4.2.1. Vector quantization in the RGB space
4.2.2. Choosing a color direction
4.3. Quaternionic context for watermarking color images
4.3.1. Quaternions and color images
4.3.2. Quaternionic Fourier transforms
4.4. Psychovisual approach to color watermarking
4.4.1. Neurogeometry and perception
4.4.2. Photoreceptor model and trichromatic vision
4.4.3. Model approximation
4.4.4. Parameters of the model
4.4.5. Application to watermarking color images
4.4.6. Conversions
4.4.7. Psychovisual algorithm for color images
4.4.8. Experimental validation of the psychovisual approach for color watermarking
4.5. Conclusion
4.6. References
5. Steganography: Embedding Data Into Multimedia Content
5.1. Introduction and theoretical foundations
5.2. Fundamental principles
5.2.1. Maximization of the size of the embedded message
5.2.2. Message encoding
5.2.3. Detectability minimization
5.3. Digital image steganography: basic methods
5.3.1. LSB substitution and matching
5.3.2. Adaptive embedding methods
5.4. Advanced principles in steganography
5.4.1. Synchronization of modifications
5.4.2. Batch steganography
5.4.3. Steganography of color images
5.4.4. Use of side information
5.4.5. Steganography mimicking a statistical model
5.4.6. Adversarial steganography
5.5. Conclusion
5.6. References
6. Traitor Tracing
6.1. Introduction
6.1.1. The contribution of the cryptography community
6.1.2. Multimedia content
6.1.3. Error probabilities
6.1.4. Collusion strategy
6.2. The original Tardos code
6.2.1. Constructing the code
6.2.2. The collusion strategy and its impact on the pirated series
6.2.3. Accusation with a simple decoder
6.2.4. Study of the Tardos code-Škorić original
6.2.5. Advantages
6.2.6. The problems
6.3. Tardos and his successors
6.3.1. Length of the code
6.3.2. Other criteria
6.3.3. Extensions
6.4. Research of better score functions
6.4.1. The optimal score function
6.4.2. The theory of the compound communication channel
6.4.3. Adaptive score functions
6.4.4. Comparison
6.5. How to find a better threshold
6.6. Conclusion
6.7. References
7. 3D Watermarking
7.1. Introduction
7.2. Preliminaries
7.2.1. Digital watermarking
7.2.2. 3D objects
7.3. Synchronization
7.3.1. Traversal scheduling
7.3.2. Patch scheduling
7.3.3. Scheduling based on graphs
7.4. 3D data hiding
7.4.1. Transformed domains
7.4.2. Spatial domain
7.4.3. Other domains
7.5. Presentation of a high-capacity data hiding method
7.5.1. Embedding of the message
7.5.2. Causality issue
7.6. Improvements
7.6.1. Error-correcting codes
7.6.2. Statistical arithmetic coding
7.6.3. Partitioning and acceleration structures
7.7. Experimental results
7.8. Trends in high-capacity 3D data hiding
7.8.1. Steganalysis
7.8.2. Security analysis
7.8.3. 3D printing
7.9. Conclusion
7.10. References
8. Steganalysis: Detection of Hidden Data in Multimedia Content
8.1. Introduction, challenges and constraints
8.1.1. The different aims of steganalysis
8.1.2. Different methods to carry out steganalysis
8.2. Incompatible signature detection
8.3. Detection using statistical methods
8.3.1. Statistical test of χ2
8.3.2. Likelihood-ratio test
8.3.3. LSB match detection
8.4. Supervised learning detection
8.4.1. Extraction of characteristics in the spatial domain
8.4.2. Learning how to detect with features
8.5. Detection by deep neural networks
8.5.1. Foundation of a deep neural network
8.5.2. The preprocessing module
8.6. Current avenues of research
8.6.1. The problem of Cover-Source mismatch
8.6.2. The problem with steganalysis in real life
8.6.3. Reliable steganalysis
8.6.4. Steganalysis of color images
8.6.5. Taking into account the adaptivity of steganography
8.6.6. Grouped steganalysis (batch steganalysis)
8.6.7. Universal steganalysis
8.7. Conclusion
8.8. References
List of Authors
Index
EULA