Security and Error Correction/Detection in 802.1x and GSM

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Security and Error Correction/Detection in 802.1x
and GSM
Hide and Seek An Introduction to Steganography
Niels Provos and Peter Honeyman,
University of Michigan

IEEE Security and
Privacy Journal, May-June 2003 (Vol. 1, No. 3)
Sweety Chauhan October 24, 2005
CMSC 691I
Clandestine Channels
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Overview

• New and Significant
• What is Steganography?
• Previous Work
• Steganographic systems for JPEG images
• Steganography Detection on the Internet
• Results

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New and Significant

• Detection of Steganographic systems via
  statistical steganalysis
• Practical application of detection algorithms

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What is Steganography?

• Art and Science of hiding communication
• A steganographic system embeds hidden content in
  unremarkable cover media
• A steganographic system consists of
• Identifying covers medium redundant bits
• Embedding process which creates a stego medium by
  replacing the redundant bits with hidden message
  data

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Statistical Steganalysis

• Modern Steganographys goal is to keep its mere
  presence undetectable
• But steganographic systems leave behind
  detectable traces in the cover medium
• Though secret content is not revealed but its
  existence can be detected
• Modifying the cover medium changes its
  statistical properties
• Eavesdroppers can detect the distortions in the
  resulting stego mediums statistical properties

The process of finding these distortions is
called statistical steganalysis
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Information Hiding Systems

• Three different aspects in information-hiding
  systems contend with each other
• Capacity amount of information that can be
  hidden in the cover medium
• Security eavesdropper inability to detect
  hidden information
• Robustness amount of modification the stego
  medium can withstand before an adversary can
  destroy hidden information
• Watermarking system high level of robustness
• Steganography high security and capacity
• Hidden information is fragile

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Steganographic Systems

• Classical Steganography system
• Security relies on the encoding systems secrecy
• e.g. Roman General shaving slaves head and
  tattooing a message on it. After the hair grew
  back, the slave was sent to deliver the hidden
  message
• Modern Steganography
• Attempts to be detectable only if secret
  information is known (secret key)
• Similar to Kerckhoffs Principle of cryptography
  which holds that a cryptographic systems
  security should rely solely on the key material

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Modern Steganography

• Steganographic communication senders and
  receivers agree on a
• steganographic system
• a shared secret key determines how message is
  encoded in the cover medium

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Overview of Encoding Step

• To send a hidden message, for example,
• Alice creates a new image with digital camera
• Alice supplies the steganographic system with her
  shared secret and message
• The steganographic systems uses the shared secret
  to determine how the hidden message should be
  encoded in the redundant bits
• The result is the stego image that Alice sends to
  Bob
• When Bob receives the image, he uses the shared
  secret and the agreed steganographic system to
  retrieve the hidden message

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Hide and Seek in JPEG images

• Why steganographic systems for JPEG format?
• System operate in a transform space
• Not affected by visual attacks (as in BMP images)
• Modifications are in the frequency domain instead
  of the spatial domain
• Neil F. Johnson and Sushil Jajodia showed
  steganographic systems for palette-based images
  leave easily detected distortions

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Discrete Cosine Transform (DCT)

• For each color component, the JPEG image format
  uses a Discrete Cosine Transform (DCT) to
  transform successive 8x8 pixel block of the image
  into 64 DCT coefficients each
• The DCT coefficients F(u, v) of an 8 x 8 block of
  image pixels f(x, y) are given by
• The following operation quantizes the
  coefficients
• where Q(u,v) is a 64-element quantization table

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Steganographic Systems

• Sequential for example JSteg
• Pseudo Random for example Outguess 0.1
• Subtraction for example F5
• Statistics aware embedding

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Sequential Embedding (I)

• Derek Uphams JSteg Algorithm - does not require
  a shared secret
• Input message, cover image
• Output stego image
• while data left to embed do
• get next DCT coefficient from cover image
• if DCT ? 0 and DCT ?1 then
• get next LSB from message
• replace DCT LSB with message LSB
• end if
• insert DCT into stego image
• end while
• As a result anyone who knows the steganographic
  system can retrieve the message hidden by JSteg

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Sequential Embedding Steganalysis (I)

• Andreas Westfeld and Andreas Pfitzmann noticed
  that
• steganographic systems that change
  least-significant bits sequentially cause
  distortions detectable by steganalysis
• for a given image, the embedding of high-entropy
  data (often due to encryption) changed the
  histogram of color frequencies in a predictable
  way.
• Embedding uniformly distributed message bits
  reduces the frequency difference between adjacent
  DCT coefficients
• By observing differences in the DCT coefficients
  frequency, embedding can be detected

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Frequency Histograms

• Histogram before (a) and after (b) a hidden
  message is embedded in a JPEG image

Sequential changes to the (a) original and (b)
modified images least-sequential bit of discrete
cosine transform coefficients tend to equalize
the frequency of adjacent DCT coefficients in the
histograms
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Sequential Embedding Steganalysis (II)

• Westfeld and Pfitzmann ?2-test
• determine whether the observed frequency
  distribution in an image matches a distribution
  that shows distortion from embedding hidden data
• The probability of embedding is determined by
  calculating p for a sample from the DCT
  coefficients
• The samples start at the beginning of the image
  and for each measurement the sample size is
  increased

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Sequential Embedding Steganalysis (III)

• A high probability of embedding indicates that
  the image contains steganographic content
• Hidden messages length can also be determined by
  JSteg

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Pseudo Random Embedding

• Niels Provoss Outguess 0.1 steganographic system
• Improves the encoding step by using a
  pseudo-random generator to select DCT
  coefficients at random
• The LSB of a selected DCT coefficient is replaced
  with encrypted message data

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Outguess 0.1 Algorithm

• The OutGuess 0.1 algorithm
• Input message, shared secret, cover image
• Output stego image
• initialize PRNG with shared secret
• while data left to embed do
• get pseudo-random DCT coefficient from cover
  image
• If DCT ? 0 and DCT ?1 then
• get next LSB from message
• replace DCT LSB with message LSB
• end if
• insert DCT into stego image
• end while

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Embedded Message Detection (I)

• ?2 -test can be extended to detect the local
  distortions in an image
• Two identical distributions produce about the
  same ?2 values in any part of the distribution
• Instead of increasing the sample size and
  applying the test at a constant position,
• a constant sample size is used and the sample
  position is increased (slided)

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Embedded Message Detection (II)
The graph shows the detection rates for three
different false-positive rates

• The extended ?2-test detects pseudo-randomly
  embedded messages in JPEG images
• The detection rate depends on
• hidden messages size
• number of DCT coefficients in an image
• can be improved by applying a heuristic that
  eliminates coefficients likely to lead to false
  negatives

The change rate refers to the fraction of
discrete cosine transform (DCT) coefficients
available for embedding a hidden message that
have been modified
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Subtraction

• Andreas Westfelds steganographic system, F5
• Instead of replacing the least-significant bit of
  DCT coefficient with message data
• F5 decrements its absolute value in a process
  called matrix encoding
• There is no coupling of any fixed pair of DCT
  coefficients
• ?2-test cannot detect F5

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Matrix Encoding

• Matrix encoding computes an appropriate (1, (2k
  1), k) Hamming code by calculating the message
  block size k from
• the message length and
• the number of nonzero non-DC coefficients
• The Hamming code (1, 2k 1, k) encodes a k-bit
  message word m into an n-bit code word a with n
  2k 1
• can recover from a single bit error in the code
  word

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The F5 algorithm

• Input message, shared secret, cover image
• Output stego image
• initialize PRNG with shared secret
• permutate DCT coefficients with PRNG
• determine k from image capacity
• calculate code word length n?2k 1
• while data left to embed do
• get next k-bit message block
• repeat
• G?n non-zero AC coefficients
• s?k-bit hash f of LSB in G
• s?s k-bit message block
• if s ?0 then
• decrement absolute value of DCT coefficient Gs
• insert Gs into stego image
• end if
• until s 0 or Gs ? 0
• insert DCT coefficients from Ginto stego image
• end while

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F5 Detection Algorithm

• Embedding information with F5 leads to double
  compression
• Most of the images are stored already in the JPEG
  format which could confuse this detection
  algorithm.
• Fridrich and her group proposed a method for
  eliminating the effects of double compression by
  estimating the quality factor used to compress
  the cover image

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Statistics-aware embedding

• Previous discussed algorithms overwrite image
  data without directly considering the distortions
  that the embedding will cause
• To embed a single bit,
• a DCT coefficients value can either increment or
  decrement which allows change of DCT
  coefficients least-significant bit in two
  different ways
• Creating groups of DCT coefficients and using the
  parity of their least-significant bits as message
  bits
• For every DCT block, the space of all possible
  changes is searched to find a configuration that
  minimizes the change to image statistics

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Detection Algorithms

• Two Different classes of algorithms
• Based on inherent statistical properties
• no need to find a representative training set
• estimate an embedded messages length
• Based on class discrimination
• Creating a representative training set is often
  difficult
• Do not provide an estimate of the hidden
  messages length

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Steganography Detection on the Internet

• How previous discussed steganalytic methods can
  be used in real world setting?
• Created a steganography detection framework that
• gets JPEG images off the Internet and
• uses steganalysis to identify subsets of the
  images likely to contain steganographic content

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Steganography Systems in use

• JSteg
• supports content encryption and compression
  before JSteg embeds the data
• uses the RC4 stream cipher for encryption
• JPHide
• uses Blowfish as a PRNG Version 0.5 supports
  additional compression of the hidden message
• uses slightly different headers to store
  embedding information
• Before the content is embedded, the content is
  Blowfish-encrypted with a user-supplied pass
  phrase
• OutGuess
• All use some form of least-significant bit
  embedding and are detectable with statistical
  analysis

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Detection Framework

• Stegdetect is an automated utility that can
  analyze JPEG images that have content hidden with
  JSteg, JPHide, and OutGuess 0.13b
• Stegdetects output lists
• the steganographic systems it finds in each image
  or
• writes negative if it couldnt detect any
• Stegdetects false-negative rate depends on
• The steganographic system and the embedded
  messages size
• The smaller the message, the harder it is to
  detect by statistical means.
• Stegdetect is very reliable in finding images
  that have content embedded with JSteg
• For JPHide, detection depends also on the size
  and the compression quality of the JPEG images

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Detection Results
Using Stegdetect over the Internet. (a) JPHide
and (b) JSteg produce different detection results
for different test images and message sizes
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Finding Images

• Images from eBay auctions and discussion groups
  in the Usenet archive for analysis.
• Developed Crawl, a simple, efficient Web crawler
  that makes a local copy of any JPEG images it
  encounters on a Web page
• Crawl performs a depth-first search and has two
  key features
• Images and Web pages can be matched against
  regular expressions
• Hence, include or exclude Web pages in the search
• Minimum and maximum image size can be specified
• Hence exclude images that are too small to
  contain hidden messages
• Calculation of true positive rate the
  probability that an image detected by Stegdetect
  really has steganographic content

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Percentages of positives for analyzed images

• After processing 2 million ebay images with
  Stagdetect
• Over 1 of all the images seemed to contain
  hidden content
• JPHide was detected most often

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Verifying Hidden Content

• Stegdetect cannot guarantee a hidden messages
  existence
• To verify the hidden content, Stegbreak must
  launch a dictionary attack against the JPEG files
• JSteg-Shell, JPHide, or Outguess all hide content
  based on a user-supplied password
• an attacker can try to guess the password by
  taking a large dictionary and trying to use every
  single word in it to retrieve the hidden message
• embedded header information, so attackers can
  verify a guessed password using header information

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Stegbreak Performance
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Results Steganography Detection on the Internet

• From eBay and Usenet research
• No single hidden message was found
• Explanations for inability to find steganographic
  content on the Internet
• All steganographic system users carefully choose
  passwords that are not susceptible to dictionary
  attacks
• Maybe images from sources that were not analyze
  carry steganographic content
• Nobody uses steganographic systems that
  researchers could find
• All messages are too small for analysis to detect

Either they are looking in the wrong place or
there is no widespread use of steganography on
the Internet
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Conclusion

• Today, computer and network technologies provide
  easy-to-use communication channels for
  steganography
• Research work
• Provides an overview of existing steganographic
  systems
• presents methods for detecting them via
  statistical steganalysis

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Future Work

• Research new algorithms to
• Hide information
• Improve Steganalysis

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References

• Hide and Seek An Introduction to Steganography,
  Niels Provos, Peter Honeyman, IEEE Security and
  Privacy Journal, May-June 2003
• Cyber warfare steganography vs. steganalysis ,
  Huaiqing Wang, Shuozhong Wang , Communications of
  the ACM, Volume 47,  Issue 10, October 2004
• http//www.outguess.org/detection.php
• http//www.jjtc.com/Security/stegtools.htm
• http//www.stack.nl/galactus/remailers/index-steg
  o.html

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Thanks a lot
For Your Presence And Patience
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Any Questions
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Homework

• Presentation Slides and Research Papers are
  available at
• www.umbc.edu/chauhan2/CMSC691I/