simple lsb embedding recap
play

Simple LSB embedding (recap) CSM25 Secure Information Hiding Dr - PowerPoint PPT Presentation

Jan 15, 2023 •227 likes •1.07k views

Simple LSB embedding (recap) CSM25 Secure Information Hiding Dr Hans Georg Schaathun University of Surrey Spring 2007 Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 1 / 34 Lesson Outcomes Chapter 2: LSB recap After this

  1. Simple LSB embedding (recap) CSM25 Secure Information Hiding Dr Hans Georg Schaathun University of Surrey Spring 2007 Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 1 / 34

  2. Lesson Outcomes Chapter 2: LSB recap After this session, everyone should be able to us Matlab to load an image and make simple changes handle binary information convert text to binary understand that any piece of information can be represented in numerous ways We may repeat material from CSM24 Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 2 / 34

  3. Review of LSB The image in the spatial domain Outline Review of LSB 1 The image in the spatial domain Numbers in Bits LSB of an image Merits and flaws Some steganalytic observations 2 The visual attack Histogramme Conclusions Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 3 / 34

  4. Review of LSB The image in the spatial domain A tiny grayscale example Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 4 / 34

  5. Review of LSB The image in the spatial domain A tiny grayscale example Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 4 / 34

  6. Review of LSB The image in the spatial domain The image file: pgm/pnm (spatial domain) P2 24 24 255 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 97 86 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36 171 85 54 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 22 0 0 52 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 0 52 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 0 52 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 0 12 77 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0 0 49 139 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36 146 132 0 0 0 32 94 15 0 0 0 0 0 0 0 0 0 0 0 0 12 111 122 65 79 99 0 0 0 0 47 23 0 0 0 0 0 0 0 0 0 0 0 73 111 128 88 22 0 79 0 0 0 0 97 49 0 0 0 0 0 0 0 0 0 36 146 132 102 26 17 0 0 19 59 0 0 0 97 49 0 0 0 0 0 0 12 111 122 65 79 19 0 0 0 0 0 0 66 13 0 77 81 0 0 0 0 0 0 73 111 128 88 22 0 0 0 0 0 0 0 0 26 101 97 103 15 0 0 0 0 36 146 132 102 26 17 0 0 0 0 0 0 0 0 0 6 37 49 11 0 0 12 49 142 149 79 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 173 77 47 41 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 37 6 57 99 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 128 137 70 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 171 49 0 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 36 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 5 / 34

  7. Review of LSB The image in the spatial domain Image formats pnm (netpbm) 8-bit grayscale (pgm) 24-bit RGB (ppm) Binary, i.e. 1-bit black/white (pbm) png (Portable Network Graphics) Kodak Photo-CD bmp Some file formats (e.g. TIFF) can carry different image formats. Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 6 / 34

  8. Review of LSB Numbers in Bits Outline Review of LSB 1 The image in the spatial domain Numbers in Bits LSB of an image Merits and flaws Some steganalytic observations 2 The visual attack Histogramme Conclusions Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 7 / 34

  9. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  10. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  11. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  12. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  13. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  14. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  15. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  16. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  17. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  18. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

  19. Review of LSB Numbers in Bits The coefficients in bits Take a number 217. Written Base 10 i.e. 217 = 7 · 10 0 + 1 · 10 1 + 2 · 10 2 Least significant digit: the last/the ones Same numer 217 10 = 11011001 2 Written in Base 2 11011001 2 = 1 · 2 0 + 0 · 2 1 + 0 · 2 2 + 1 · 2 3 + 1 · 2 4 + 0 · 2 5 + 1 · 2 6 + 1 · 2 7 Least significant bit: the last/the ones Any base could be used. (8 and 16 are common.) Dr Hans Georg Schaathun Simple LSB embedding (recap) Spring 2007 8 / 34

Recommend

Graph Drawing Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 )

Graph Drawing Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 )

Graph Drawing Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 ) assigns each vertex a coordinate and each edge a (not necessarily straight) line connecting the corresponding coordinates. 0 1 3 4 1 1 0 2 5 2

541 views • 18 slides
Planarity Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 ) assigns

Planarity Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 ) assigns

Planarity Embedding Embedding For a given graph G = ( V , E ) , an embedding (into R 2 ) assigns each vertex a coordinate and each edge a (not necessarily straight) line connecting the corresponding coordinates. 0 1 3 4 1 1 0 2 5 2 5

754 views • 18 slides
Sorting Simple Sorting Algorithm (Recap) A[0] A[i] A[i+1] A[N-1] for in range(len(A)) : k =

Sorting Simple Sorting Algorithm (Recap) A[0] A[i] A[i+1] A[N-1] for in range(len(A)) : k =

Sorting Simple Sorting Algorithm (Recap) A[0] A[i] A[i+1] A[N-1] for in range(len(A)) : k = position of min. element between A [i] and A [N-1] Swap A [i] and A [k] Selection Sort Courtesy Prof P R Panda CSE, IIT Dellhi 2 Simple Sorting

407 views • 22 slides
Greedy embedding of a graph Greedy embedding of a graph 99 Greedy embedding Greedy embedding

Greedy embedding of a graph Greedy embedding of a graph 99 Greedy embedding Greedy embedding

Given a graph, find an embedding s.t. greedy routing works Greedy embedding of a graph Greedy embedding of a graph 99 Greedy embedding Greedy embedding Given a graph G, find an embedding of the vertices in R d , s.t. for each pair of

361 views • 23 slides
Improving Knowledge Graph Embedding Using Simple Constraints Boyang Ding, Quan Wang , Bin Wang, Li

Improving Knowledge Graph Embedding Using Simple Constraints Boyang Ding, Quan Wang , Bin Wang, Li

Improving Knowledge Graph Embedding Using Simple Constraints Boyang Ding, Quan Wang , Bin Wang, Li Guo Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences Code and

1.01k views • 25 slides
A Simple Java Code Generator for ACL2 Based on a Deep Embedding of ACL2 in Java Alessandro

A Simple Java Code Generator for ACL2 Based on a Deep Embedding of ACL2 in Java Alessandro

A Simple Java Code Generator for ACL2 Based on a Deep Embedding of ACL2 in Java Alessandro Coglio Kestrel Institute Workshop 2018 ATJ Java code generator for ACL2 (ACL2 To Java) based on AIJ deep embedding of ACL2 in Java (ACL2 In Java)

901 views • 38 slides
PARTNERSHIPS FOR CHILDREN Branding and Positioning :: FINAL WORKSHOP RECAP WORKSHOP RECAP //

PARTNERSHIPS FOR CHILDREN Branding and Positioning :: FINAL WORKSHOP RECAP WORKSHOP RECAP //

PARTNERSHIPS FOR CHILDREN Branding and Positioning :: FINAL WORKSHOP RECAP WORKSHOP RECAP // WHAT WE DID WORKSHOP RECAP // WHAT WE HEARD Our results are above and beyond theres a We like simple, straightforward messages.

763 views • 29 slides
Lecture 2.2: PAT Embedding Andreas Hinzmann Annapaola de Cosa PAT Tutorial, June

Lecture 2.2: PAT Embedding Andreas Hinzmann Annapaola de Cosa PAT Tutorial, June

Lecture 2.2: PAT Embedding Andreas Hinzmann Annapaola de Cosa PAT Tutorial, June 2010 Embedding in PAT The Embedding in PAT is a very useful tool to perform a physics analysis in a light way. In order to save

287 views • 7 slides
Simple near-optimal auctions Posted price auctions Recap Last week: Optimal revenue single

Simple near-optimal auctions Posted price auctions Recap Last week: Optimal revenue single

Algorithmic game theory Ruben Hoeksma December 17, 2018 Simple near-optimal auctions Posted price auctions Recap Last week: Optimal revenue single item auction (Myersons auction) Virtual valuations For any feasible (DSIC, IR)

747 views • 13 slides
Which lens spaces embed in S 4 ? Proposition L ( p , q ) , | p | > 1 , does not embed in S 4 .

Which lens spaces embed in S 4 ? Proposition L ( p , q ) , | p | > 1 , does not embed in S 4 .

Embedding 3-manifolds in S 4 Ahmad Issa University of Texas at Austin 1 Embedding manifolds in R n Note: a manifold (of dim < n ) embeds in R n if and only if it embeds in S n . Whitney embedding theorem (1943) A compact smooth n -manifold

1.51k views • 129 slides
Geometric Probability Distributions MDM4U: Mathematics of Data Management Recap A simple game is

Geometric Probability Distributions MDM4U: Mathematics of Data Management Recap A simple game is

p r o b a b i l i t y d i s t r i b u t i o n s p r o b a b i l i t y d i s t r i b u t i o n s Geometric Probability Distributions MDM4U: Mathematics of Data Management Recap A simple game is played, where the player must roll a 1 or a 6 on a

182 views • 3 slides
Coding Theorems for Reversible Embedding Frans Willems and Ton Kalker T.U. Eindhoven, Philips

Coding Theorems for Reversible Embedding Frans Willems and Ton Kalker T.U. Eindhoven, Philips

Coding Theorems for Reversible Embedding Frans Willems and Ton Kalker T.U. Eindhoven, Philips Research DIMACS, March 16-19, 2003 Outline 1. Gelfand-Pinsker coding theorem 2. Noise-free embedding 3. Reversible embedding 4. Robust and

507 views • 33 slides
Unit 6: Simple Linear Regression Lecture : Introduction to SLR Statistics 101 Thomas Leininger

Unit 6: Simple Linear Regression Lecture : Introduction to SLR Statistics 101 Thomas Leininger

Unit 6: Simple Linear Regression Lecture : Introduction to SLR Statistics 101 Thomas Leininger June 17, 2013 Recap: Chi-square test of independence Recap: Chi-square test of independence 1 Ball throwing Expected counts in two-way tables

1.53k views • 108 slides
Efficient Distributed Workload (Re-)Embedding Monika Stefan Stefan Henzinger Neumann Schmid

Efficient Distributed Workload (Re-)Embedding Monika Stefan Stefan Henzinger Neumann Schmid

Efficient Distributed Workload (Re-)Embedding Monika Stefan Stefan Henzinger Neumann Schmid Many Years Ago Single server Systems were fixed and workload-agnostic Simple communication patterns (if at all), endpoints fixed

686 views • 67 slides
Embedding 3-manifolds via surgery on surfaces Kyle Larson University of Texas at Austin

Embedding 3-manifolds via surgery on surfaces Kyle Larson University of Texas at Austin

Embedding 3-manifolds via surgery on surfaces Embedding 3-manifolds via surgery on surfaces Kyle Larson University of Texas at Austin klarson@math.utexas.edu June 12th, 2015 Embedding 3-manifolds via surgery on surfaces Embedding 3-manifolds

967 views • 54 slides
Avoiding artifacts in spectral white matter fiber clustering and embedding Demian Wassermann

Avoiding artifacts in spectral white matter fiber clustering and embedding Demian Wassermann

Spectral embedding algorithms Spectral embedding and clustering pipeline Conclusions Work in progress Avoiding artifacts in spectral white matter fiber clustering and embedding Demian Wassermann & Rachid Deriche Odyss ee project -

910 views • 37 slides
T ree Embedding via the Generalized Loewner Equation Vivian Olsiewski Healey University of

T ree Embedding via the Generalized Loewner Equation Vivian Olsiewski Healey University of

T ree Embedding via the Generalized Loewner Equation Vivian Olsiewski Healey University of Chicago Joint work with Govind Menon KIAS, June 19, 2018 Preview: Main Idea Just heard from Peter Lin: Embedding the CRT as a limit of embeddings of

568 views • 33 slides
Making EDSLs fly TechMesh London 2012-Dec-05 Lennart Augustsson Standard Chartered Bank

Making EDSLs fly TechMesh London 2012-Dec-05 Lennart Augustsson Standard Chartered Bank

Making EDSLs fly TechMesh London 2012-Dec-05 Lennart Augustsson Standard Chartered Bank lennart@augustsson.net Wednesday, 5 December 12 Plan A simple EDSL Shallow embedding Deep embedding LLVM code generation Wednesday, 5 December 12 A

588 views • 44 slides
Convergence of simple Triangulations Marie Albenque (CNRS, LIX, Ecole Polytechnique) Louigi

Convergence of simple Triangulations Marie Albenque (CNRS, LIX, Ecole Polytechnique) Louigi

Convergence of simple Triangulations Marie Albenque (CNRS, LIX, Ecole Polytechnique) Louigi Addario-Berry (McGill University Montr eal) Journ ees Cartes, 20th June 2013 Planar Maps Triangulations. A planar map is the embedding of

1.39k views • 100 slides
Outline Definition Upward Embedding Horizontal Torus ( T h ) Previous works

Outline Definition Upward Embedding Horizontal Torus ( T h ) Previous works

Upward Embedding on T h Ardeshir Dolati dolati@shahed.ac.ir may 13, 2008 CTW2008 Gargnano Italy Ardeshir Dolati Outline Definition Upward Embedding Horizontal Torus ( T h ) Previous works Upward embedding on plane and

322 views • 21 slides
Clairvoyant embedding in one dimension P eter G acs Computer Science Department Boston

Clairvoyant embedding in one dimension P eter G acs Computer Science Department Boston

Clairvoyant embedding in one dimension P eter G acs Computer Science Department Boston University Spring 2012 The embedding problem Given m > 0 and infinite 0-1 sequences x , y we say y is m -embeddable in x , if there exists an

640 views • 28 slides
Rby : An Embedding of Alloy in Ruby Aleksandar Milicevic , Ido Efrati, and Daniel Jackson

Rby : An Embedding of Alloy in Ruby Aleksandar Milicevic , Ido Efrati, and Daniel Jackson

Rby : An Embedding of Alloy in Ruby Aleksandar Milicevic , Ido Efrati, and Daniel Jackson {aleks,idoe,dnj}@csail.mit.edu 1 What exactly is this embedding? 2 What exactly is this embedding? alloy API for ruby? 2 What exactly is this

990 views • 83 slides
An Introduction to Hilbert Space Embedding of Probability Measures Krikamol Muandet Max Planck

An Introduction to Hilbert Space Embedding of Probability Measures Krikamol Muandet Max Planck

An Introduction to Hilbert Space Embedding of Probability Measures Krikamol Muandet Max Planck Institute for Intelligent Systems T ubingen, Germany Jeju, South Korea, February 22, 2019 1/34 Reference Kernel Mean Embedding of

953 views • 79 slides
Large-Scale Clustering through Functional NCut Embedding Embedding Experiments Summary

Large-Scale Clustering through Functional NCut Embedding Embedding Experiments Summary

Large-Scale Clustering Ratle, Weston & Miller Introduction Large-Scale Clustering through Functional NCut Embedding Embedding Experiments Summary Frdric Ratle Jason Weston Matthew L. Miller IGAR - University of

417 views • 18 slides

More recommend