Center for Information Security Technologies, Korea University Digital Image Steganalysis Kwang-Soo Lee
Jan 11, 2016
Center for Information Security Technologies, Korea University
Digital Image Steganalysis
Kwang-Soo Lee
Outline
Steganography
LSB Steganography
LSB Steganalysis
Cryptography
Cryptography scrambles a message to obscure its meaning.
Today secure communication is often identified with cryptography.
However, cryptography reveals the fact that communication is
happening. ???
@2*$#d(*%7*
Steganography
The word “steganography” comes from Greek, steganos and graphein.
Steganography is the art of hiding information in ordinary-looking objects.
Steganography aims to conceal the existence of secret communication.
Classical Steganography
Examples:
Hidden tattoo,
Covered writing,
Invisible ink,
Microdots,
Character arrangement,
Paper mask,
etc.
Hiding a secret message in physical objects.
Secrecy depends on keeping the methods secret.
Modern Steganography
Hiding information in digital objects, Invisibly.
The Invisibility must depend on just the stego-key, not the stego
system.
LSB Steganography
Replacing least-significant-bits (LSBs) of digital data with message
bits.
Using digital multimedia, such as image, audio, video, as cover-
objects.
Embedding random message bits in LSBs will not cause any
discernable difference from the cover-signals.
Easy to implement, High payloads.
11001000Extracting
Embedding
Digital Images for Steganography
Types of digital images:
binary, gray-scale, RGB color, palette, JPEG, etc.
The LSB plane of image data looks like random noise.
Bit-plane decomposition of the Lena image in gray-scale.
lena.bmp 6th Bit Plane 4th Bit Plane LSB Plane
LSB Steganalysis
Steganalysis is the science of detecting hidden messages in digital signals.
It takes advantage of statistical or perceptual distinction of stego-signals fro
m cover-signals.
LSB steganalysisVisual attack, histogram analysis (PoV analysis),Closed color analysis,Regular-singular (RS) analysis,Sample pair (SP) analysis,LR Cube analysis,Etc.
PoV analysis
Proposed by Westfeld and Pfizmann (IH 1999) .
PoV means a pair of values which differ just in the LSBs.0 1 2 3 4 5 6 7 8 9 10 ……
LSB embedding tends to equalize those frequencies of the values of each Po
V.
cover-image histogram stego-image histogram
LSB
Em
bed
din
g
0.00
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0 1 2 3 4 5 6 7 8 9 10
Pixel value
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lativ
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req
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ncy
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Pixel value
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Sample Pair Analysis
Proposed by Dumitrescu et al. (IH 2003)
Based on symmetry of quantized noise distribution.
Take advantage of spatial correlation such as pixel adjacency.
Estimate the length of hidden message.
Outperform PoV analysis.
cover-image histogram stego-image histogram
LSB
Em
bed
din
g
0.00
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0.30
0 1 2 3 4 5 6 7 8 9 10
Pixel value
Re
lativ
e f
req
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ncy
0.00
0.05
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0 1 2 3 4 5 6 7 8 9 10
Pixel value
Re
lativ
e f
req
ue
ncy
LR Cube Analysis
Left and Right cube analysis (LRCA), developed by us (IH 2005)
Our method uses high dim. vectors as basic units drawn from
digital signals.
Consider the vector noise distribution and its distortion of LSB
embedding.
LR Cube Analysis
Left cube and Right cube, and the possible cube patterns or complexities.
Cover-signals show similar complex levels between the left cubes and the
right cubes, but these are not the case for stego-signals after the LSB
embedding
LRCA works by measuring the similarities between these two distributions.
Center for Information Security Technologies, Korea University
Thank youKwang-Soo Lee
kslee@cist.korea.ac.kr