PPTX - Unicamp

Report
Forensic Detection of Image
Manipulation Using Statistical
Intrinsic Fingerprints
Fernando Barros
Filipe Berti
Gabriel Lopes
Marcos Kobuchi
Seminar Series
MO447 - Digital Forensics
Prof. Dr. Anderson Rocha
[email protected]
http://www.ic.unicamp.br/~rocha
Outline
Outline
‣ Introduction
‣ System Model and Assumptions
‣ Statistical Intrinsic Fingerprints of Pixel Value
Mappings
‣ Detecting Contrast Enhancement
‣ Detecting Additive Noise in Previously JPEGCompressed Images
‣ Conclusion
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Introduction
Nowadays...
‣ In recent years, digital images have become
increasingly prevalent through society.
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© Daily Stormers (www.dailystomers.com)
© Raíz da Vida (www.raizdavida.com.br)
Real
Pictures
© http://topwalls.net/grand-canyon-united-states-2/
© www.wallpea.com
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Digital
Images
© Fan Pop (www.fanpop.com)
© johnnyslowhand.deviantart.com
© www.highqualitywallpapers.eu
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Fake
Pictures!
©igossip.com
© fashmark.files.wordpress.com
© www.epicfail.com
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© www.hoax-slayer.com
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Fake
Pictures?
© Veja (http://veja.abril.com.br/)
© Telegraph (www.telegraph.co.uk)
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© www.vidrado.com
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© http://www.buzzfeed.com/tomphillips/22-viral-pictures-that-were-actually-fake
Fake Pictures?
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Fake
Pictures?
© http://10steps.sg/inspirations/photography/70-strange-photos-that-are-not-photoshopped/
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Consequence
‣ At present, an image forger can easily alter a digital
image in a visually realistic manner.
‣ As a result, the field of digital image forensics has
been born.
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State Of The Art
‣ Identification of images and image regions which have
undergone some form of manipulation or alteration
‣ No universal method of detecting image forgeries
exists
‣ Different techniques, with their own limitations
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Some techniques
‣ Lighting Angle Inconsistencies
‣ Inconsistencies in chromatic aberration
‣ Absence of Color Filter Array (CFA) interpolation
induced correlations
‣ Classifier based approaches
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Fingerprints
‣ Most image altering operation leave behind distinct,
traceable “fingerprints” in the form of image alteration
artifacts
‣ Because these fingerprints are often unique to each
operation, an individual test to catch each type of
image manipulation must be designed
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Some works with fingerprints
‣ Resampling
‣ Double JPEG compression
‣ Gamma correction
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This work
‣ Pixel value mapping leaves behind statistical artifacts
which are visible in an image’s pixel value histogram
‣ By observing the common properties of the histogram
of unaltered images, it’s possible to build a model of
an unaltered image’s histogram
2013 Seminar Series – Digital Forensics (MO447/MC919)
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This work
‣ A number of operations are in essence pixel value
mapping, it’s proposed a set of image forgery
detection techniques which operate by detecting the
intrinsic fingerprint of each operation
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System Model
and Assumptions
Digital image
‣ In this work, digital images created by using an
‣
‣
electronic imaging device to capture a real world
scene
Each pixel is assigned a value by measuring the light
intensity reflected from a real world scene
Inherent in this process is the addition of some zero
mean sensor noise which arises due to several
phenomena (shot noise, dark current, etc)
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Color image
‣ For color images, it is often the case that the light
‣
‣
passes through a CFA so that only one color
component is measured at each pixel location in this
fashion
In that case, the color component not observed at
each pixel are determined through interpolation
At the end of this process the pixel values are
quantized, then stored as the unaltered image
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Histogram
‣ h(l) can be generated by creating L equally spaced
bins which span the range of possible pixel values
‣ Tabulate the number of pixels whose value falls
within the range of each bin
‣ Gray levels values in P = {0, … , 255}, Color values in
P³
‣ Pixel value histogram uses 256 bins
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Histograms
‣ None of the histograms contains sudden zeros or
impulsive peaks
‣ Do not differ greatly from the histogram’s envelope
‣ To unify these properties, pixel value are described as
interpolatably connected
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Interpolatably connected
‣ Any histogram value h(l) can be aproximated by ĥ(l)
‣ Each value of ĥ has been calculated by removing a
particular value from h then interpolating this value
using a cubic spline
‣ Little difference from h and ĥ
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Fig. 1. Left: Histogram of a typical image. Right: Approximation of the histogram at left by
sequentially removing then interpolating the value of each histogram entry.
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System Model
‣ To justify this model, a database of 341 unaltered
images captured using a variety of digital cameras
‣ Obtained each image’s pixel value histogram h and its
approximated histogram ĥ
‣ The mean squared error between both along with the
signal power of h to obtain an SNR~30.67dB
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Statistical
Intrinsic
Fingerprints of
Pixel Value
Mappings
Pixel Value Mapping
‣ A number of image processing operations can be
specified entirely by a pixel value mapping
‣ Leave behind distinct, forensically significant artifacts,
which we will refer as intrinsic fingerprint
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Intrinsic Fingerprint
‣
Original: x; Tampered: y
‣ Intrinsic Fingerprint
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Discrete Fourier Transform
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Example: Histogram
Synthesized Image
2013 Seminar Series – Digital Forensics (MO447/MC919)
Real World Image
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Example: DFT
Synthesized Image
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Real World Image
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Example: Frequency Domain
Synthesized Image
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Real World Image
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Example: Frequency Domain
Synthesized Image
2013 Seminar Series – Digital Forensics (MO447/MC919)
Real World Image
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Intrinsic Fingerprints
‣ When examining a potentially altered image, if the
histogram of unaltered pixel values is known, the
tampering fingerprint can be obtained using
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But...
‣ In most real scenarios, one has no a priori knowledge
of an image’s pixel value histogram, thus the
tampering fingerprint cannot be calculated
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However
‣ It’s possible to ascertain the presence of a tampering
‣
fingerprint by determining identifying features of a
mapping’s intrinsic fingerprint
Searching for their presence in the histogram of the
image
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Detecting
Contrast
Enhancement
Overview
‣ Contrast Enhancement operations seek to increase
the dynamic range of pixel values within images.
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Detection of Globally Applied
Contrast Enhancement
‣ Usually nonlinear mappings
‣ Consider only monotonic pixel value mappings.
‣ Thereby, disconsidering simple reordering mappings.
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Globally Applied Contrast
‣ Two significant mappings
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Globally Applied Contrast
‣ Euclidian norm increases!
‣ The energy of the DFT as well
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Globally Applied Contrast
‣ Therefore, all contrast enhancement mappings result
in an increase in energy.
‣ This energy is related to the intrinsic fingerprint.
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Globally Applied Contrast
‣ Expected DFT’s to be strongly low-pass signal.
‣ Therefore, the presence of energy in the high
frequency regions is indicative of contrast
enhancement.
‣ Contrast enhancement will cause isolated peaks and
gaps in the histogram.
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Globally Applied Contrast
‣ Saturation Case
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http://www.funnyjunk.com/funny_pictures/3306291/Houston+we+have+a+problem/
Globally Applied Contrast
‣ Solution
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Globally Applied Contrast
‣ Measuring the energy
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Globally Applied Contrast
‣ Defining the best c with 244 images e N = 4
p
‣ γ = 1.1
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Globally Applied Contrast
‣ Results
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Globally Applied Contrast
‣ Database of 341 unaltered images, taken in different
resolutions and light conditions
‣ The green color layer created the grayscale images.
‣ γ ranging from 0.5 to 2.0
‣ 4092 grayscale images
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Globally Applied Contrast
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Globally Applied Contrast
‣N
p
= 4 e c = 112
‣P
d
of 99% at a Pfa approximately of 3% or less
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Locally Applied Contrast
‣ Defined as applying a contrast mapping to a set of
contiguous pixels within an image.
‣ Can identify cut-and-paste forgeries.
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Locally Applied Contrast
‣ To detect it, the image is divided in smaller blocks and
the global technique is applied to the blocks.
‣ Who small(and big!?) are these blocks?
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Locally Applied Contrast
‣ Test the 341 unaltered images and use γ ranging from
0.5 to 0.9.
‣ Blocks of size 200x200, 100x100, 50x50, 25x25, and
20x20
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Locally Applied Contrast
‣ The contrast enhancement can be reliably detected
using testing blocks sized 100x100 pixels with a Pd of
at least 80% in every case at a Pfa of 5%.
‣ When γ ranged
from 1.0 to 2.0, the Pd was of 95% at
a Pfa of 5%
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Locally Applied Contrast
‣ In order to test the copy-and-paste, Photoshop was
used to create the image (c).
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Locally Applied Contrast
‣ The image was divided in 100x100 pixel blocks and
tested local contrast enhancement on the red(d),
green(e) and blue(f) color layers.
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Locally Applied Contrast
‣ The image was divided in 50x50 pixel blocks and
tested local contrast enhancement on the 3 the color
layers.
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Locally Applied Contrast
‣ Applying a detection
criteria.
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Histogram Equalization
‣ Histogram equalization effectively increases the
dynamic range of an image’s pixel values by
subjecting them to a mapping such that the
distribution of output pixel values is approximately
uniform.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Histogram Equalization
‣ In order to identify it, we calculate the “uniformity” of
the histogram.
‣ The process will introduce zeros into an image’s pixel
value histogram, so mean absolute differences and
mean square differences won’t work.
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2013 Seminar Series – Digital Forensics (MO447/MC919)
http://www.funnyjunk.com/funny_pictures/3306291/Houston+we+have+a+problem/
[2]
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Histogram Equalization
‣ For histogram equalized saturated images, the
location of the impulsive component is often shifted.
‣ Suppose that the number of pixels in the lowest bin is
greater than 2N/255.
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Histogram Equalization
‣ If the lowest l such that h(l) > 0 is greater or equal to 1
and h(l) > 2N/255, the image is identified as saturated.
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Histogram Equalization
‣ Test the 341 unaltered images and the 341 histogram
equalized images.
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Histogram Equalization
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Histogram Equalization
‣ Analyze the frequency domain.
‣ α(k) is a weighting function used to deemphasize the
high frequency regions in H(k)
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Histogram Equalization
‣ Best conditions using α
(k) was with r1 = 0.5,
obtaining Pd of 99% with a Pfa of 0.5% and a Pd of
100% with a Pfa of 3%.
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Histogram Equalization
‣ Test 2046 images.
‣ r =4
‣ P of 100%
2
d
with a Pfa of 1%.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Detecting
Additive Noise in
Previously JPEGCompressed
Images
Additive Noise
‣ Additive noise can be used to mask previous
modifications to images.
‣ Previous techniques has dealt with detection of
localized fluctuations of SNR in an image.
‣ Fail on detection of globally added noises.
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Additive Noise
‣ This technique applies a predefined mapping with a
known fingerprint to a potentially altered image.
‣ If some noise was intentionally added, then an
identifying feature of this fingerprint will be absent.
‣ We’ll be able to detect the presence of an additive
noise if application of mapping does not introduce a
fingerprint with this feature.
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Scale and Round mapping
‣ For additive noise detection it’ll be used scale and
round mapping:
‣ And the set
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Scale and Round mapping
‣ Cardinality of U (v) is periodic in v with period p.
‣ So, the intrinsic fingerprint of scale and round
C
operation will contain a periodic component with
period p.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Hypothesis Testing Scenario
‣ JPEG compression/decompression schematics
© Compressed Examples by JISC Digital Media. All files © University of Bristol, 2009
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Hypothesis Testing Scenario
‣ So, if a monotonically increasing mapping is applied to
any color layer in the YCbCr color space, that
mapping’s fingerprint will be introduced into the
histogram of the color layer value.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Hypothesis Testing Scenario
‣ Final stage of JPEG decompression: pixel
transformation from YCbCr to RGB, mathematically
described by this equation:
‣ Values less than 0 is set to 0 and greater than 255 is
set to 255.
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Hypothesis Testing Scenario
‣ Defining:
‣ Detection of additive noise can be formulated as an
statistical hypothesis testing problem:
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Hypothesis Testing Scenario
‣ The fingerprint left by the mapping:
‣ helps to rewrite both hypothesis as:
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Hypothesis Testing Scenario
‣ Under hypothesis H
0
, zi can be expressed as:
‣ The term round(cx ) dominates the behavior of z and,
i
i
so, the number of distinct xi values mapped to each zi
value will occur in a fixed periodic pattern.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Hypothesis Testing Scenario
‣ This will result in a periodic pattern discernible in the
histogram, which corresponds to the intrinsic scale
and round mapping.
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Hypothesis Testing Scenario
‣ Under hypothesis H
1
2013 Seminar Series – Digital Forensics (MO447/MC919)
, zi has a different behavior:
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Hypothesis Testing Scenario
‣ This hypothesis leads to 3 additional terms containing
scale and round mapping, each with their own scaling
constant.
‣ If this constants and the original scaling has no
common period, no periodic pattern will be introduced
into the histogram, as can be observed in the figures.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Hypothesis Testing Scenario
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Additive Noise Detection
Images
‣ Detection of the addition of noise to a previously
JPEG-compressed images is the same as detection of
the periodic fingerprint within the normalized
histogram.
‣ This detection is well suited for frequency domain and
produces peaks with arbitrary location.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Additive Noise Detection
Images
‣ Applying DFT in the normalized and pinched-off
histogram we obtain Gzi, it is possible to measure the
strength of the peak introduced into it:
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Additive Noise Detection
Images
‣ Then it is used the following decision rule to determine
presence of additive noise:
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Additive Noise Detection
Images - 1st performance test
‣ 227 unaltered images from 4 different digital cameras
from unique manufacturers.
‣ Diversity of JPEG-compressed images using camera’s
settings.
‣ Set of altered images created by decompression and
addition of unit variance Gaussian noise to each pixel
value.
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Additive Noise Detection
Images - 1st performance test
‣ All altered saved with original images resulting in a DB
of 554 images.
‣P
= 80% @ Pfa = 0,4% (if Pfa <= 6,5% Pd goes to
nearly 99%).
d
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Additive Noise Detection
Images - 1st performance test
2013 Seminar Series – Digital Forensics (MO447/MC919)
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Additive Noise Detection
Images – 2nd performance test
‣ 244 JPEG-compressed images at different quality.
ratios.
‣ Q=90, 70, 50 and 30.
‣ Again, unit variance Gaussian noise added.
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Additive Noise Detection
Images – 2nd performance test
‣ For images with Q >= 50.
‣ P = 99% @ P = 3,7%.
d
fa
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Additive Noise Detection
Images – 2nd performance test
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Conclusions
Conclusions
‣ Statistical Intrinsic Fingerprints of Pixel Value
Mappings.
‣ Detecting Contrast Enhancement
‣ Detecting Additive Noise in Previously JPEGCompressed Images
2013 Seminar Series – Digital Forensics (MO447/MC919)
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References
References
1.
A. Swaminathan, M.Wu, and K. J. R. Liu, “Digital image forensics via intrinsic fingerprints,” IEEE Trans. Inf.
Forensics Security, vol. 3, no. 1, pp. 101–117, Mar. 2008.
1.
http://www.jiscdigitalmedia.ac.uk/guide/file-formats-and-compression/
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Thank You!
Obrigado!

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