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E-grāmata: Advanced Statistical Steganalysis

  • Formāts: PDF+DRM
  • Sērija : Information Security and Cryptography
  • Izdošanas datums: 09-Aug-2010
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642143137
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  • Formāts: PDF+DRM
  • Sērija : Information Security and Cryptography
  • Izdošanas datums: 09-Aug-2010
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642143137

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This is the first book dedicated to modern steganography and steganalysis. The author offers a proposal to structure approaches to provably secure steganography according to their implied assumptions on the limits of the adversary and on the nature of covers.



Steganography is the art and science of hiding information in inconspicuous cover data so that even the existence of a secret message is kept confidential, and steganalysis is the task of detecting secret messages in covers. This research monograph focuses on the role of cover signals, the distinguishing feature that requires us to treat steganography and steganalysis differently from other secrecy techniques.The main theoretical contribution of the book is a proposal to structure approaches to provably secure steganography according to their implied assumptions on the limits of the adversary and on the nature of covers. A further contribution is the emphasis on dealing with heterogeneity in cover distributions, crucial for security analyses. The author's work complements earlier approaches based on information, complexity, probability and signal processing theory, and he presents numerous practical implications. The scientific advances are supported by a survey of the classical steganography literature; a new proposal for a unified terminology and notation that is maintained throughout the book; a critical discussion of the results achieved and their limitations; and an assessment of the possibility of transferring elements of this research's empirical perspective to other domains in information security.The book is suitable for researchers working in cryptography and information security, practitioners in the corporate and national security domains, and graduate students specializing in multimedia security and data hiding.

Recenzijas

''This excellent book provides a detailed account of recent advances in statistical steganalysis ... This book provides a valuable resource for security practitioners, researchers, and graduate students''. (Alessandro Berni, ACM Computing Reviews, 3/3/2011)

1 Introduction
1(10)
1.1 Steganography and Steganalysis as Empirical Sciences
1(1)
1.2 Objective and Approach
2(2)
1.3 Outline
4(7)
Part I Background and Advances in Theory
2 Principles of Modern Steganography and Steganalysis
11(68)
2.1 Digital Steganography and Steganalysis
11(3)
2.1.1 Steganographic System
12(1)
2.1.2 Steganalysis
13(1)
2.1.3 Relevance in Social and Academic Contexts
13(1)
2.2 Conventions
14(3)
2.3 Design Goals and Metrics
17(5)
2.3.1 Capacity
17(1)
2.3.2 Steganographic Security
18(3)
2.3.3 Robustness
21(1)
2.3.4 Further Metrics
22(1)
2.4 Paradigms for the Design of Steganographic Systems
22(4)
2.4.1 Paradigm I: Modify with Caution
22(1)
2.4.2 Paradigm II: Cover Generation
23(2)
2.4.3 Dominant Paradigm
25(1)
2.5 Adversary Models
26(4)
2.5.1 Passive Warden
26(2)
2.5.2 Active Warden
28(2)
2.6 Embedding Domains
30(9)
2.6.1 Artificial Channels
30(1)
2.6.2 Spatial and Time Domains
31(1)
2.6.3 Transformed Domain
31(4)
2.6.4 Selected Cover Formats: JPEG and MP3
35(3)
2.6.5 Exotic Covers
38(1)
2.7 Embedding Operations
39(10)
2.7.1 LSB Replacement
40(1)
2.7.2 LSB Matching (±1)
41(4)
2.7.3 Mod-k Replacement, Mod-k Matching, and Generalisations
45(2)
2.7.4 Multi-Sample Rules
47(1)
2.7.5 Adaptive Embedding
48(1)
2.8 Protocols and Message Coding
49(8)
2.8.1 Public-Key Steganography
50(3)
2.8.2 Maximising Embedding Efficiency
53(4)
2.9 Specific Detection Techniques
57(7)
2.9.1 Calibration of JPEG Histograms
57(3)
2.9.2 Universal Detectors
60(3)
2.9.3 Quantitative Steganalysis
63(1)
2.10 Selected Estimators for LSB Replacement in Spatial Domain Images
64(12)
2.10.1 RS Analysis
64(3)
2.10.2 Sample Pair Analysis
67(2)
2.10.3 Higher-Order Structural Steganalysis
69(4)
2.10.4 Weighted Stego Image Steganalysis
73(3)
2.11 Summary and Further Steps
76(3)
3 Thwards a Theory of Cover Models
79(32)
3.1 Steganalyst's Problem Formalised
79(5)
3.1.1 The Plausibility Heuristic
79(2)
3.1.2 Application to Digital Steganography
81(2)
3.1.3 Incognisability of the Cover Distribution
83(1)
3.2 Cover Models
84(9)
3.2.1 Defining Cover.Models
84(2)
3.2.2 Options for Formulating Cover Models
86(2)
3.2.3 Cover Models and Detection Performance
88(4)
3.2.4 Summary and Motivations for Studying Cover Models
92(1)
3.3 Dealing with Heterogeneous Cover Sources
93(4)
3.3.1 Mixture Distributions
95(1)
3.3.2 The Mixture Cover Model
95(2)
3.4 Relation to Prior Information-Theoretic Work
97(9)
3.4.1 Theoretical Limits
98(1)
3.4.2 Observability Bounds
99(2)
3.4.3 Computational Bounds
101(1)
3.4.4 Applicability of the Theory of Cover Models
102(2)
3.4.5 Indeterminacy in the Cover
104(2)
3.5 Instances of Cover Models for Heterogeneous Sources
106(1)
3.6 Summary
107(4)
Part II Specific Advances in Steganalysis
4 Detection of Model-Based Steganography with First-Order Statistics
111(16)
4.1 Fundamentals of Model-Based Steganography
111(3)
4.2 MB1: An Embedding Function for JPEG Covers
114(3)
4.3 Detection Method
117(3)
4.4 Experimental Validation
120(3)
4.5 Summary and Outlook
123(4)
4.5.1 Limitations and Future Directions
124(1)
4.5.2 Possible (Short-'harm) Countermeasures
125(1)
4.5.3 Implications for More Secure Steganography
126(1)
5 Models of Heterogeneous Covers for Quantitative Steganalysis
127(28)
5.1 Metrics for Quantitative Steganalysis
128(13)
5.1.1 Conventional Metrics
128(2)
5.1.2 Improved Metrics Based on a Distribution Model
130(5)
5.1.3 Decomposition of Estimation Errors
135(6)
5.2 Measurement of Sensitivity to Cover Properties
141(10)
5.2.1 Method
142(2)
5.2.2 Modelling the Shape of the Between-Image Distribution
144(4)
5.2.3 Modelling the Shape of the Within-Image Distribution
148(3)
5.3 Summary and Conclusion
151(4)
6 Improved Weighted Stego Image Steganalysis
155(28)
6.1 Enhanced WS for Never-Compressed Covers
155(17)
6.1.1 Enhanced Predictor
156(5)
6.1.2 Enhanced Calculation of Weights
161(5)
6.1.3 Enhanced Bias Correction
166(1)
6.1.4 Experimental Results
166(6)
6.2 Adaptation of WS to JPEG Pre-Compressed Covers
172(10)
6.2.1 Improved Predictor
173(3)
6.2.2 Estimation of the Cover's JPEG Compression Quality
176(1)
6.2.3 Experimental Results
177(5)
6.3 Summary and Outlook
182(1)
7 Using Encoder Artefacts for Steganalysis of Compressed Audio Streams
183(26)
7.1 MP3 Steganography and Steganalysis
183(6)
7.1.1 Problem Statement in the Mixture Cover Model Framework
185(1)
7.1.2 Level of Analysis and Related Work
185(2)
7.1.3 Method
187(2)
7.2 Description of Features
189(7)
7.2.1 Features Based on the Compression Size Control Mechanism
190(2)
7.2.2 Features Based on Model Decisions
192(2)
7.2.3 Features Based on Capability Usage
194(1)
7.2.4 Feature Based on Stream Formatting
195(1)
7.3 Experimental Results for Encoder Detection
196(5)
7.3.1 Single-Compressed Audio Files
196(3)
7.3.2 Importance of Individual Features
199(1)
7.3.3 Influence of Double-Compression
199(2)
7.4 Experimental Results for Improved Steganalysis
201(1)
7.5 Explorative Analysis of Encoder Similarities
202(2)
7.6 Summary and Discussion
204(5)
7.6.1 Limitations and Future Directions
204(1)
7.6.2 Transferability to Other Formats
205(1)
7.6.3 Related Applications
206(3)
Part III Synthesis
8 General Discussion
209(10)
8.1 Summary of Results
209(3)
8.1.1 Results Based on Informal Arguments
209(1)
8.1.2 Results Based on Mathematical Proofs
210(1)
8.1.3 Results Based on Empirical Evidence
211(1)
8.2 Limitations
212(1)
8.3 Directions for Future Research
213(2)
8.3.1 Theoretical Challenges
214(1)
8.3.2 Empirical Challenges
214(1)
8.3.3 Practical Challenges
215(1)
8.4 Conclusion and Outlook
215(4)
A Description of Covers Used in the Experiments 219(4)
B Spurious Steganalysis Results Using the 'van Hateren' Image Database 223(4)
C Proof of Weighted Stego Image (WS) Estimator 227(2)
D Derivation of Linear Predictor for Enhanced WS 229(2)
E Game for Formal Security Analysis 231(2)
F Derivation of ROC Curves and AUC Metric for Example Cover Models 233(2)
G Supplementary Figures and Tables 235(10)
References 245(18)
List of Tables 263(3)
List of Figures 266(3)
List of Acronyms 269(3)
List of Symbols 272(6)
List of Functions 278(2)
Index 280