Transcript Digital Steganography

Digital Image Steganalysis
Kwang-Soo Lee
Center for Information Security Technologies, Korea University
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.
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@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.
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Embedding
Extracting
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.
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
from cover-signals.
LSB steganalysis
Visual 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.
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LSB embedding tends to equalize those frequencies of the values of each
PoV.
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Relative frequency
LSB Embedding
Relative frequency
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Pixel value
Pixel value
cover-image histogram
stego-image histogram
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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.
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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.
Thank you
Kwang-Soo Lee
[email protected]
Center for Information Security Technologies, Korea University