Project presentation - Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE

Steganography application of effective
Genetic Shifting Algorithm against RS
Analysis
Advisor: MScEE, Vladislav Kaplan
Vadim Purinson LV Tailoring Software

Who owns the information – owns the
world
Rotschild Nathan Mayer
• What is Steganography?
• Why Steganography?
• History of Steganography.
• Digital steganography.
• Steganalysis.
• RS Fridrich analysis.
• Genetic shifting algorithm.
• Software implementation.
• Tests
• Conclusions
7/16/2015LV Tailoring Software
2

What is Steganography?
The art and science of hiding information by embedding it in some
other media.
• Steganography versus cryptography
• In general, steganography approaches hide a
message in a cover e.g. text, image, audio file,
etc.
3

Why Steganography?
• Transfer secret information or embed secret
messages into media.
• Data, intellectual property and privacy
protection - Digital Water marking, medical
data.
4

History of Steganography.
• 400 B.C. – writings of Herodotus
• 1499 – “Steganographia”, Trithemius –
steganography and magic.
• 1665 – Steganographica, Gaspari Schotti.
• 1870 – The Pigeon Post into Paris.
Most popular example in history this is use
invisible inks.
5

Terminology and Definitions
•
6
Equation 3
Equation 4

Terminology and Definitions
• Steganographic system or stegasystem –
this is set of tools and methods are used to
generate a secret channel of information
transmission.
7

Digital image definition
A digital image is binary representation of a two
dimensional image and contains a fixed number of rows
and columns of pixels.
• Pixel
• Byte
• Bit
8
Equation 1

Message embedding
mathematical definition
8 bit Grayscale equivalents to 1 byte per pixel.
For example, for the image size of 7 Kbyte
maximum message size can be embedded, by
using 1 LSB is 7168 bit
9
Equation 2

Digital steganography.
Advantages of digital images steganography is:
• There are a variety of methods used in which information can be hidden in
the images.
• Relatively large volume of digital images representation, that allows the
embedding of large amount of information.
• Known size of the cover media, that absence of restrictions, requirements
imposed by real-time.
• Presence of relatively large textural regions in most digital images that have
noise structure and well suited for information integration.
• Weak sensitivity of the human eye to minor changes the color of the image,
brightness, contrast and the noise presence.
• Image steganography has come quite far with the development of fast,
powerful graphical computers.
10

IMAGE STEGANOGRAPHY TECHNIQUES
• Least Significant Bit insertion – LSB
• Masking and filtering
11

The simple LSB schema
12

LSB - Example
13
Lena Söderberg

LSB - Example
“A” = 65(decimal) = 10000001 (binary)
14

Classification of Steganography
Methods.
• Substitution methods in spatial domain = LSB;
• Transform domain = frequency domain;
• Spread spectrum techniques;
• Statistical methods;
• Distortion techniques;
• Cover generation methods;
15

The properties of the human eye
used in the steganography .
• selectivity to brightness fluctuations;
• frequency sensitivity;
• masking effect;
16

Selectivity to brightness fluctuations
17
Human eye sensitivity to
contrast.
Experimental data by Aubert (1865), Koenig and
Brodhun (1889) and Blanchard (1918). It
indicates that the Weber-Fechner law - according
to which the smallest perceptible change in
intensity ∆ 𝐼 vs. intensity level I is constant.

Frequency sensitivity
18
Sensitivity of eye for the colors

Masking effect
19
Herman Grid

Steganalysis
• Steganalysis
• Steganalyst
• Attack on steganography system
20

Stegattacks classes
• Attack with the knowledge of the modified media only.
• Attack with knowledge of unmodified container.
21
The results of stegattack
• Detect secret message presence.
• Recover secret message from stegoimage.
• Destroy the message in case no possibility to recover
message.

Steganalysis hierarchy
22

Main methods of stegattack
• Visual analysis – detect visual image
degradation by “naked” eye.
• Statistical Histogram and STD analysis.
• Detection methods are based on data hiding
analyzing the characteristics of the probability
distribution of the container.
23

Visual Attacks
24
Grayscale image
visual attack example
Grayscale image filter
visual attack example

Histogram Analysis Attack
25

Statistical Analysis Attack
• Stego Only Attack;
• Known Cover Attack;
• Known Message Attack;
• Blind Steganalysis;
• Semi-blind;
26

Fridrich RS Analysis
𝐹𝑚 1 ↔ 2,3 ↔ 4, … 223 ↔ 224
𝐹−𝑚 0 ↔ 1,2 ↔ 3, … 224 ↔ 225
• “regular”
• “singular”
• “unchanged”
27
← 𝑅−𝑚 𝑆−𝑚 →
𝑅 𝑚 ↔ 𝑆 𝑚𝑅 𝑚 > 𝑆 𝑚
𝑅−𝑚 > 𝑆−𝑚

Genetic Shifting Algorithm (Shen Wang )
28
Before or After ?
𝐶 =
𝑖
𝑁
𝑖 + 1 − 𝑖
𝑁 − 1
𝑁 – this is number of pixels, and (𝑖 + 1) and
𝑖 are indicate current and next pixel values.

Introduction to LabVIEW
29

Front Panel
30
Example of Front panel view
• Modern
• System
• Classic
• Express
• Control
Design &
Simulation
• .Net &
ActiveX
• Signal
processing
• Add ons
• User
Controls
• Select and
control
• DSC
Module
• RF
Communica
tions
• Sound &
Vibration
• Vision
Controls palette view

Block diagram
31
Function paletteExample of Block diagram view

More functions
• Tools palette • Operating tool
• Positioning tool
• Labeling tool
• Wiring tool
32
• Wiring
• SubVis

Basic LabVIEW Hierarchy
33

Front panel view
34
1. Source cover image
2. Result stegoimage
3. Cover Histogram and STD statistic window
4. Stegoimage Histogram and STD statistic window
5. RS analysis results on Stegoimage
6. LSB level to be used (up to LSB-4)
7. Start shifting (GSM)
8. Standard deviation evaluation
9. Snaked array length.
10. Open output result text message
11. Decode message from stegoimage
12. Encode message into cover message
13. Stop button
14. Start / stop menu

Steganography directory view
35

Encoding Decoding Example
36

Steps definition
• Perform basic message coding (Cover Image) up to LSB-4 for gray images.
• Perform basic message recovery (Stego Image) up to LSB-4 for gray images.
• Compare visual image degradation.
• Compare visual degradation through common tools (Histogram, STD).
• Perform study of coded message saturation (message of different length) vs.
recovery and image degradation per different LSB coding at gray images.
• Build RS analysis (Fridrich algorithm) routine.
• Confirm validity of RS analysis on gray images.
• Implement secure genetic steganography method for RS baseline shifting
for LSB-1. (GSM for RS shifting).
• Perform basic message recovery with GSM for RS shifting for LSB-1.
• Perform RS analysis comparison for different message length with GSM for
RS shifting and without, use different “snake” division array image
representation.
37

Compare visual image degradation
38
LSB-1
The recovered text file size is 6.21 KB
(6,361 bytes), 1177 words text,
equivalent to 2.5 pages in WORD
format.

Compare tool
39

40
LSB-2
equivalent to 5 pages in WORD
format.

41
LSB-3
(19,106 bytes), 3317 words text, equivalent
to 7.5 pages in WORD format.

42
LSB-4
equivalent to 10 pages in WORD format –
this is a maximum text file size can be
imbedded into 225 × 225 𝑝𝑖𝑥𝑒𝑙𝑠 image by
using 4LSB plane.

Compare visual degradation through
common tools
43

common tools
44
Different depth of the LSB
Blue line is displays Cover
image Histogram and red line
represents manipulated image
distribution.

common tools
45
Histogram degradation trough of
message enlargement for 4LSB level

common tools
46
STD view

Build RS analysis (Fridrich algorithm)
routine.
47

routine
48
LSB-1

𝑅 𝑚/𝑆 𝑚 differences versus message
volume.
49
Image1
Message % of Rm % of Sm diff
Message
length(bytes) % of image
0 54.9 45.1 9.8 0 0
1 53.8 46.2 7.6 699 1.05
2 52.1 47.9 4.2 2,225 3.35
3 51.5 48.5 3 4,055 6.11
4 51.4 48.6 2.8 4,222 6.36
5 50.8 49.2 1.6 6,201 9.34
6 49.8 50.2 -0.4 8,169 12.3
Image2
0 54.2 45.8 8.4 0 0
1 53.9 46.1 7.8 699 1.78
2 52.5 47.5 5 2,225 5.67
3 51.8 48.2 3.6 4,055 10.34
4 51.7 48.3 3.4 4,222 10.76
5 50 50 0 6,201 15.8
6 49.8 50.2 -0.4 6,361 16.33
Image3
0 52.5 47.5 6 0 0
1 52 48 5 699 1.05
2 51.8 48.2 4.4 2,225 3.35
3 51.9 48.1 4 4,055 6.11
4 51.7 48.3 4 4,222 6.36
5 50.9 49.1 3.4 6,201 9.34
6 50.8 49.2 0.6 8,169 12.31

routine
50

Image 2 RS analysis results.
51
2LSB
Message
% of
Rm % of Sm diff
Message length(bytes)
% of
image
0 54.2 45.8 8.4 0 0
1 54 46 8 699 1.78
2 53.4 46.6 6.8 2,225 5.67
3 52.8 47.2 5.6 4,055 10.34
4 52.7 47.3 5.4 4,222 10.76
5 52.1 47.9 4.2 6,201 15.8
6 50.9 49.1 1.8 12,737 24.31
3LSB
0 54.2 45.8 8.4 0 0
1 54.2 45.8 8.4 699 1.78
2 54.2 45.8 8.4 2,225 5.67
3 53.9 46.1 7.8 4,055 10.34
4 53.9 46.1 7.8 4,222 10.76
5 53.4 46.6 6.8 6,201 15.8
6 52 48 4 19,106 36.47
4LSB
0 54.2 45.8 8.4 0 0
1 54.1 45.9 8.2 699 1.78
2 54 46 8 2,225 5.67
3 53.9 46.1 7.8 4,055 10.34
4 53.9 46.1 7.8 4,222 10.76
5 53.4 46.6 6.8 6,201 15.8
6 50.3 49.7 0.6 25,477 48.56

Build RS analysis (Fridrich algorithm) routine (30
images 225 × 225 𝑝𝑖𝑥𝑒𝑙𝑠, 8𝑏𝑖𝑡 𝑑𝑒𝑝𝑡ℎ)
52

Secure genetic steganography method
53

13 and 29 division snaked array LSB-1
RS analysis results
54
Image 1
Message
length(bytes)
% of imager
0 63 37 26 0 0
1 60.9 39.1 21.8
699 1.33
2 56.9 43.1 13.8
2,225 4.24
3 54.5 45.5 9 4,055 7.74
4 54.5 45.5 9 4,222 8.06
5 52.4 47.6 4.8 6,201 11.83
6 51.1 48.9 2.2 6,361 12.14
Image 2
0 60 40 20 0 0
1 58.9 41.1 17.8
699 1.05
2 57.1 42.9 14.2
2,225 3.35
3 55.3 44.7 10.6
4,055 6.11
4 55.5 44.5 11 4,222 6.36
5 51.7 48.3 3.4 6,201 9.34
6 51.1 48.9 2.2 8,169 12.3
Image3
0 58.1 41.9 16.2
0 0
1 56.6 43.4 13.2
699 1.05
2 55 45 10 2,225 3.35
3 54.2 45.8 8.4 4,055 6.11
4 54.3 45.7 8.6 4,222 6.36
5 53.3 46.7 6.6 6,201 9.34
6 50.9 49.1 1.8 8,169 12.31
Image 1
Message
length(bytes)
% of
image
0 58.7 41.3 17.4 0 0
1 57 43 14 699 1.05
2 54.5 45.5 9 2,225 3.35
3 53 47 6 4,055 6.11
4 52.8 47.2 5.6 4,222 6.36
5 51.2 48.8 2.4 6,201 9.34
6 50.5 49.5 1 8,169 12.3
Image 2
0 56.9 43.1 13.8 0 0
1 56 44 12 699 1.33
2 53.7 46.3 7.4 2,225 4.24
3 52.1 47.9 4.2 4,055 7.74
4 52.1 47.9 4.2 4,222 8.06
5 49.9 50.1 -0.2 6,201 11.83
6 49.7 50.3 -0.6 6,361 12.14
Image3
0 54.9 45.1 9.8 0 0
1 53.8 46.2 7.6 699 1.05
2 52.7 47.3 5.4 2,225 3.35
3 52.6 47.4 5.2 4,055 6.11
4 52.5 47.5 5 4,222 6.36
5 52.1 47.9 4.2 6,201 9.34
6 50.6 49.4 1.2 8,169 12.31
13 division snaked array 29 division snaked array

Secure genetic steganography method
55
No shifted 13 division snake array
29 division snake array 51 division snake array

Conclusions
56

Thank you for your attention
7/16/2015
57
LV Tailoring Software

Project presentation - Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie Project presentation - Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE

Ähnlich wie Project presentation - Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE (20)

Mehr von Vladislav Kaplan

Mehr von Vladislav Kaplan (9)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Project presentation - Steganographic Application of improved Genetic Shifting algorithm against RS analysis - BScEE final assignment by Vadim Purinson, adviser Vladislav Kaplan MScEE