Iaetsd a review on modified anti forensic

A REVIEW ON MODIFIED ANTI FORENSIC
TECHNIQUE FOR REMOVING DETECTABLE
TRACES FORM DIGITAL IMAGES
1
M.GOWTHAM RAJU. 2
N.PUSHPALATHA,
M.Tech (DECS) Student, Assistant professor
Department of ECE, AITS Department of ECE, AITS
Annamacharya Institute of Technology and Sciences, Tirupati, India-517520
1
mgr434@gmail.com
2
pushpalatha_nainaru@rediffmail.com
Abstract: The increasing attractiveness and trust on
digital photography has given rise to new acceptability
issues in the field of image forensics. There are many
advantages to using digital images. Digital cameras
produce immediate images, allowing the photographer
to outlook the images and immediately decide whether
the photographs are sufficient without the
postponement of waiting for the film and prints to be
processed. It does not require external developing or
reproduction. Furthermore, digital images are easily
stored. No conventional "original image" is prepared
here like traditional camera. Therefore when forensic
researchers analyze the images they don’t have access
to the original image to compare. Fraud through
conventional photograph is relatively difficult,
requiring technical expertise. Whereas significant
features of digital photography is the ease and the
decreased cost in altering the image. Manipulation of
digital images is simpler. With some fundamental
software, digitally-recorded image can easily be edited.
The most of the alterations include borrowing, cloning,
removal and switching parts of a digital image. A
number of techniques are available to verify the
authenticity of images. But the fact is that number of
image tampering is also increasing. The forensic
researcher’s need to find new techniques to detect the
tampering. For this purpose they have to find the new
anti-forensic techniques and solutions for them. In this
paper a new anti-forensic technique is considered,
which is capable of removing the evidences of
compression and filtering. It is done by adding a
specially designed noise called tailored noise to the
image after processing. This method can be used to
cover the history of processing in addition to that it can
be also used to remove the signature traces of filtering.
Keywords: Digital forensic, jpeg compression, image
coefficients, image history, filtering, Quantization, DCT
coefficients.
Introduction
Digital images become very popular for transferring visual
information. And there are many advantages using these
images instead of traditional camera film. The digital
cameras produce instant images which can be viewed
without delay of waiting for film processing. it does not
require external development they can be store easily. And
there should not be taken any time delay. Images can be
processed in different ways. They are processed as jpeg
images, in some other cases they are processed in bit mat
format. When they are used in bitmap format it does need
to use without any information of past processing. to know
about the past processing information it is desirable to
know the artifacts of image. These techniques are capable
of finding the earlier processing information. Therefore
forensic researches need to examine the authenticity of
images to find how much the trust can be put up on the
techniques and this can also be used to find out the
drawback of this techniques. Person with good knowledge
in image processing can do undetectable manipulation. it is
also desirable to find the draw backs of these techniques.
For this purpose research has to develop both forensic and
anti-forensic techniques to understand the weaknesses.
Consider the situation that already tried to remove the
artifacts of compression. The forensic experts can easily
find out the existing techniques such as quantized
estimation. It is useful when image processing unit receives
compression details and quantization table used for
processing and compression. Some of the existing
techniques like detection of blocking signature estimation
of quantization table this allow the mismatches and
forgeries in jpeg blocks by finding the evidence of
compression. To solve this problem of image forensic the
research has to develop tools that are capable of fooling the
existing methodologies. Even though the existing methods
have advantages some limitations too. The main drawback
of these techniquesis that they do not report for the risk that
new technique may be design and used to conceal the
traces ofmanipulations. As mention earlier it may possible
for an image forger to generate undetectable compression
and other image forgeries. This modified anti-forensic
technique approach is presented which is capable of hiding
the traces of earlier processing including both compression
and filtering. This concept is that adding specially designed
noise to the image’s blocks will help to hide the proof of
tampering.
18
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1. RELATED TO PROJECT WORK:
1.1. ANTI FORENSIC OF DIGITAL IMAGE
COMPRESSION:
As society has become increasingly reliant upon digital
image to communicate visual information, a number of
forensic techniques have developed. Among the most
successful of these are techniques that make use of an
images compression history and its associate compression
finger prints. Anti-forensic techniques capable of fooling
forensicAlgorithms this paper represents set of anti-
forensic techniques designed to remove forensically
significant indicators of compression of an image. in this
technique first distributes the image transform coefficients
before compression then adding anti-forensic transform
coefficients of compressed image so that distribution
matches estimation one. When we use these frame work of
anti-forensic techniques specially targeted at erased finger
prints left by both JPEG and wavelet based coders.
1.1.1. ANTI-FORENSIC FRAMEWORK:
All image compression techniques are subbing band
coders, which are themselves a subset of transform coders.
Transform coders are mathematically applying to the
signals of compressing the transforms coefficients. Sub
band coders are transform coders that decompose the signal
in to different frequency bands. By applying two
dimensional invertible transform, such as DCT to as image
as a whole that has been segmented into a series of disjoint
sets. Each quantized transform coefficient value can be
directly related to its corresponding original transform
coefficient value by equation.
= ≤ < + 1 (1)
If the image was divided into segment during compression,
another compression finger print may arise. Because of the
loss
Fig1: anti forensic of digital image compression
When the anti-forensically modify each quantized
transform coefficient by adding specially designed noise,
which we refer to as anti-forensic dither, to it value
according totheequation
Z=Y+D
The segment length is equal to the length of the
quantization interval the probability that the quantized
coefficient value is qk is given by.
( = ) ∫ ( , ) (2)
The anti-forensic dither’s distribution is given by the
formula
P (D=d)=
( , )
∫ ( . )
( ≤ + < + 1)(3)
1.1.2. JPEG ANTI-FORENSICS:
Brief over view of JPEG compression then present our
anti-forensic technique designed to remove compression
finger prints from JPEG compressed image DCT
coefficients. For gray scale image, JPEG compression
begins by segmenting an image into a series of non over
lapping 8x8 pixel blocks then computing the two
dimensional DCT of each block. Dividing each coefficient
value by its corresponding entry in predetermined
quantization matrix rounding the resulting value to the
nearest integer. First image transformed from the RGB to
the YCBCrcolorspace. After this can been performed,
compression continues as if each color layer were an
independent gray scale image.
1.1.3. DCT Coefficient Quantization Fingerprint
Removal:
Anti-forensic frame work which we
outlined in section 2.we begins by modeling the
distribution of coefficients values with in a particular ac
sub band using the Laplace distribution.
( = ) = x (4)
Using this model and the quantization rule
described above the coefficient values of an ac sub band of
DCT coefficients with in a JPEG compressed image will be
distributed according to the discrete Laplace distribution.
P(Y=y)=
1 − , ⁄
if y = 0
sin ( 2)
0
if y=kQi,j (5)
19
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Fig2: Histogram of perturbed DCT coefficient values from
a DCT sub band in which all coefficients were quantized to
zero during JPEG compression.
Wavelet-Based Compression Overview:
Through several wavelet-based image compression
techniques exists such as SPIHT,EZW,and most popularly
JPEG 2000.they all operate in a similar fashion and leave
behind similar compression finger prints.JPEG 2000 begins
compression by first segmenting an image into fixed sized
non over lapping rectangular blocks known as “tiles” while
other operate on the image as a whole.
Two dimensional DWT of the image or
each image tile is computed these sub bands of the wave let
coefficient.
Because of these sub bands corresponding
to either high or low frequency DWT coefficients in each
spatial dimension, the four sub bands are referred to using
the notation LL, LH, HL, and HH.
Image compression techniques achieve loss compression
through different processes they each introduce DWT
coefficient quantization finger prints into an image
Quantization and dequantization process causes DWT
coefficient in image compression in the multiples of their
respective sub bands.
As a result only the n most significant bits of
each DWT coefficients are retained. This is equivalent to
applying the quantization rule. Where X is a DWT
coefficient from an uncompressed imager y is the
corresponding DWT coefficient in its SPIHT compressed
counterpart.
Fig3: Top: Histogram of wavelet coefficient from an
uncompressed image.
Bottom: wavelet coefficient from same image after SPIHT
compression.
Fig4: Top: peg compressed image using quality factor.
Bottom: Anti forensically modified version of same image.
2. UNDETECTABLE IMAGE TAMPERING
THROUGH JPEG COMPRESSION
Number of digital image forensic techniques have been
developed which are capable of identifying an image’s
origin, tracing its processing history, and detecting image
forgeries. Though these techniques are capable of
identifying standard image manipulation, they do not
address the possibility t be that anti forensic operations may
be designed and used to hide evidence of image tampering
.we propose anti-forensic operation capable of removing
blocking artifacts from a previously JPEG compressed
image. We can show that by help of this operation along
another anti-forensic operation we are able to fool forensic
20
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methods designed to detect evidence of JPEG compression
in decoded images, determine an image’s origin, detect
double JPEG compression, and identify cut and paste
image forgeries.
A digital image forgery has resulted in an
environment where the authenticity of digital images
cannot be trusted. Many of these digital forensic techniques
rely on detecting artifacts left in image by JPEG
compression. Because most of the digital cameras make use
of proprietary quantization tables, an image compression
history can be used to help identify the camera used to
capture it. These techniques are quite adept at detecting
standard image manipulation, they do not account for the
possibility that anti-forensic operation designed to hide
traces of image manipulation may applied to an image.
Recent work as shown such operations can be constructed
to successfully fool existing image forensic techniques.
Back Ground:
When an image is subjected to JPEG compression, it is first
segmented into 8X8 pixel blocks. The DCT of each block
is computed and resulting set of DCT coefficients are
quantized by dividing each coefficient by its corresponding
entry in a quantization table then rounding the result to the
nearest integer. The set of quantized coefficients read into a
single bit stream and lossless encoded. so decompressed
begins by bit stream of quantized DCT coefficients and
reforming into a set of 8X8 pixel blocks.
As a result two forensically significant
artifacts are left in an image by JPEG compression. That is
DCT coefficient quantization artifact sand blocking
artifacts. Blocking artifacts are the discontinuities which
occur across 8X8 pixel block boundaries because of
JPEG’s loss nature antiforensic technique capable of
removing DCT coefficient artifacts from a previously
compressed image.
2.1. ANTI-FORENSIC DEBLOCKING OPERATION
JPEGblocking artifacts must be removed from an image
after anti-forensic dither has been applied to its DCT
coefficients. Number of de blocking algorithms proposed
since the introduction of JPEG compression, these are all
suited for anti-forensic purposes. To be successful it must
remove all visual and statistical traces of block anti-facts.
We found that light smoothing an image followed by
adding low-power white Gaussiannoise. Able to remove
statistical traces of JPEG blocking artifacts without causing
the image DCT coefficient distribution to deviate from the
Laplace distribution. in the anti-forensically deblocked
image according to the equation.
A measure of blocking artifacts strength is
obtained by calculating the difference between the
histograms of Z’ and Z” values denoted by H1 and H2
respectively, using the equation.
K=∑|HI (Z′= n) −HII (Z′′= n)|.
The values of K lying above a fixed detection threshold
indicate the presence of blocking artifacts.
Fig5: Histogram of DCT coefficients from an image before
compression (top left), after JPEG compression (top right),
and after addition anti-forensic dither to the coefficients of
the JPEG compressed image.
2.2. IMAGE TAMPERING THROUGH ANTI-
FORENSIC:
We show that anti-forensic dither and our proposedanti-
forensic deblocking operation can be used to deceive
several existing image forensic algorithms that rely on
detecting JPEG compression artifacts.
21
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Fig 6:Result of the proposed anti-forensic deblocking
algorithm applied to a typical image after it has been
JPEG compression using a quality factor of 90 (far left),
70(center left), 30(center right),and 10 (far right) followed
by the addition of anti-forensic dither to its DCT
coefficients.
2.3. Hiding Traces of Double JPEG compression:
An image forger may wish to remove evidence of
corresponding a previously JPEG compressed image. Such
image forger wishes to alter a previously compressed
image, and then save the altered image as JPEG.Several
methods have been proposed to detect recompression of
JPEG compressed image commonly known as double
JPEG compression.
2.4. Falsifying an Image’s Origin:
In some scenarios, an image forger may wish to falsify the
origin of digital image simply altering the Mata data tags
associated with an image’s originating device is insufficient
to accomplish this because several origin identifying
features are intrinsically contained with a digital
image.Anti-forensic dither of an image’s DCT coefficient,
then re-compressing the image using quantization tables
associated with another device. by doing an image in this
manner, we are able to insert the quantization signature
associated with a different camera into an image while
preventing the occurrence of double JPEG compression
artifacts that may alert forensic investigators of such a
forgery.
Fig 7: Histogramof (3, 3) DCT coefficients from an image
JPEG compressed once using a quality factor of 85(left),
image after being double JPEG compressed using a quality
factor of 75 followed by 85(center),and the image after
being JPEG compressed using a quality factor of
75,followed by the application of anti –forensic dither, then
recompressed using a quality factor of 85(right).
3. PROPOSED METHOD:
Tothe best knowledge increased in the field of anti-
forensics. Most of the methods of this an forensics is
to find out the process that which the image
compression is takes places, such of that methods
involves in like JPEG detection and quantization table
estimation.in this method of anti-forensic the JPEG
compression of an image history also produces the
information of camera used to produce an image.
Although it can be used to discover the forged areas
along with in the picture.in case of image
compression this technique is also developed to use as
evidence of image manipulation.so in this anti
forensic technique traces left by compression and
other processing are discussed
4. CONCLUSION:
By the above two existing methods, one of the
method of anti-forensic method of digital image
compression it has increasingly up on digital images
to communicate and this method is considered anti
forensics method is fooling forensic algorithms. This
technique is designed to remove forensically
significant indicators of compression of an image.
First developing frame work its design the anti-
forensic techniques to remove compression finger
prints from image transform coefficients. This anti
forensic dither to the transform coefficient of
compressed image distribution matches the estimated
one. When we use this frame work it specifically
targeted at erasing compression finger prints left by
both JPEG and wavelet based coders. These
techniques are capable of removing forensically
detectable traces of image compression without
significantly impacting an image’s visual quality.
The second method of undetectable image tampering
through JPEG compression anti forensics digital
22
ISBN: 378 - 26 - 138420 - 5
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forensics are developed which are capable of
identifying an image’s origin. Thesetechniques are
capable of identifying standard image manipulations.
This anti forensic technique capable of removing
blocking artifacts from previously JPEG compression
image.in this method we are able to fool forensic
methods to designed to detect evidence of JPEG
compression in decoded images, determine an
image’s origin.
When comparing above two existing methods, the
anti-forensic method of removing detectable traces
from digital images has advanced technique increases
attractive ness and more over trust in the digital
images it has capable of removing evidences of
compression and filtering of in digital images history
processing.by adding tailored noise in the image
processing we can find out the where the images is
tampered and compressed, weather its fake or original
this can be used in the medical department as well as
in the police department cases. This method is to be
used to cover history of processing and it can be also
used to remove the signature traces of filtering.
REFERENCES
[1]M.chen, J.fridrich, M.goljan and Lukas, “Determining
image origin and integrity using sensor noise” IEEE
trans.inf.forensic security, vol.3, no.1, pp.74-90,
march.2008.
[2]A.swaminathan,M.Wu,andK.R>Liu,”Digital image
forensics via intrinsic finger
prints,”IEEEtrans.inf.forensicssecurity, vol.3, no.1, pp.101-
117, mar.2008.
[3] M.Kirchner and R.Bohme,”Hiding traces of resampling
in digital images,”IEEEtrans.inf.forensics security, vol.3,
no.4, pp.582-592, Dec.2008.
[4] I. Ascribes, S. Bayram, N.Memon, M. Ram Kumar, and
B. Sankur, “A classifier design for detecting image
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Oct. 2004, vol. 4, pp. 2645–2648.
[5] M. C. Stamm and K. J. R. Liu, “Forensic detection of
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[6] Z. Fan and R. de Queiroz, “Identification of bitmap
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[7] M. C. Stamm and K. J. R. Liu, “Wavelet-based image
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pp. 1737–1740.
[8] W. S. Lin, S. K. Tajo, H. V. Zhao, and K. J. Ray Liu,
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[9] H. Farid, “Digital image ballistics from JPEG
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[10] A.C. Popes cu and H. Farid, “Statistical tools for
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[11] T. Pevny and J. Fridrich, “Detection of double-
compression in JPEG images for applications in
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[12] M. Kirchner and R. Bohme, “Hiding traces of
resampling in digital images,” IEEE Trans. Information
Forensics and Security, vol. 3, no.4, pp. 582–592, Dec.
2008.
23
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