Transcript Document

How Realistic is Photorealistic?
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How Realistic is Photorealistic?
Yaniv Lefel
Hagay Pollak
Based on the work of - Siwei Lyu and Hany Farid
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Introduction
• Among the set of all possible images,
natural images only occupy a tiny
subspace.
• For instance, there are totally 256^(n^2)
different 8-bit grayscale images of size nxn
pixels.
Natural images are sparsely distributed in
the space of all possible images.
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Image space
•e.g. when n = 10 pixels, it results in 1.3x10^154 different images4 !!!
Introduction (cont’)
• The regularities within natural images can
be modeled statistically.
• Image statistical models are already in use
by applications such as:
Compression, de-noising, segmentation,
texture synthesis, content-based retrieval
and object/scene categorization.
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Motivation 1
Identify Computer Graphics
• Sophisticated computer graphics software
can generate highly convincing
photorealistic images able to deceive the
human eye.
• Differentiating these two types of images is
an important task to ensure the
authenticity and integrity of photographs.
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Computer graphics example
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Motivation 2
Identify Steg Images
• Image steganography hides messages in digital
images in a non-intrusive way that is hard to
detect visually.
• The task of generic steganalysis is to detect the
presence of such hidden messages without the
detailed knowledge of the embedding methods.
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Steganography example
Original
Steg
|Original-Stego|
message
• Steg is the message image embedded into the original image.
• The rightmost image is the absolute value of the difference
between the original and steg image, normalized into 8 bit for
display purposes.
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How ? Example
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Motivation 3
Identify Re-broadcasting
• Biometrics-based (e.g., face, iris, or voice)
authentication and identification systems
are vulnerable to the “rebroadcast”
attacks. (e.g. using a high-resolution
photograph of a human face).
• We need to differentiate a “live” image
(captured in real time by a camera) and a
“rebroadcast” one (a photograph).
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How to distinuish images ?
• Image properties ?
– Image intensity histogram
– Image frequency
Other method ?
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Using known methods
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Why wavelets
• Image representations based on multi-scale
image decomposition (e.g., wavelets)
decompose an image with basis functions
partially localized in both space and
frequency - a compromise between these
representations.
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QMF - Quadrature Mirror Filter
• The QMF pyramid decomposition splits the
image frequency space into three different
scales, and within each scale, into three
orientation subbands (Vertical, Horizontal
and Diagonal).
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QMF diagram
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QMF
• The vertical, horizontal and diagonal
subbands at scale i are denoted by Vi(x; y),
Hi(x; y), and Di(x; y), respectively.
• Can be generated by convolving the
image, I(x, y), with low-pass and high-pass
filters.
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QMF decomposition – Example 1
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QMF decomposition – Example 2
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Example – QMF statistics
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Add some magic …
• QMF
coefficients
Simple but long
(and out of scope)
mathematical
procedure
• Magic Box
• Error
coefficients
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Technique Diagram
Feature vector 
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Computing the Feature Vector
• 3 – Sub-bands (vertical, horizontal, diagonal).
• 3 – Scales (levels of decompositions).
• 4 – First order statistics (mean, variance, skewness
– asymmetry measure, kurtosis).
• 3 – Colors (RGB)
• 2 – marginal statistics (wavelet coefficients),
error statistics.
• 216 = 3*3*4*3*2
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Image examples
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Feature vectors projected on 3D space
Natural image – Blue.
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Synthetic images - noise (Green), fractal (Black), and discs (Red)
Learning and Testing
CG\Steg\rebroadcast
• CG\steg\rebroadcast images are prepared.
• Statistics is collected over natural images and
CG\steg\rebroadcast images (not using color).
• A Machine learning system (e.g. FLD, LDA,
SVM) is then trained on some of the natural and
some of the CG\steg\rebroadcast images.
• The remaining natural and CG\steg\rebroadcast
images are used for testing.
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Natural vs. CG results (SVM)
All
Train
images
Succ Test
[%]
Succ
[%]
Natural 40000
32000 70.9 8000 66.8
CG
4800
6000
99.1 1200 98.8
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Training the system
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Photorealistic (CG) images
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The Impact of Color
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Correctly Classified Photorealistic
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Incorrectly Classified Photographic
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Natural vs. Steganography images
• A message consists of a 64x64 pixel region
of a random image chosen from the same
image database.
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Natural vs. Steganography results
All
Train Succ Test
images
[%]
Succ
[%]
Natural 1000
750
99.5
250
98.9
Steg
750
98.3
250
97.6
1000
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Live vs. rebroadcast
• We collect statistics from natural images
and the same images after having been
printed on a laser printer and re-scanned
with a scanner (printing and scanning are
done at 72 dpi).
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Live vs. rebroadcast results
All
Train Succ Test
images
[%]
Succ
[%]
Natural 1000
750
99.5
250
99.5
rebroad 200
cast
150
100
50
99.8
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Live vs. rebroadcast (cont’)
• Remark: It is not surprising that printing
significantly disturbs the image statistics.
Detecting a rebroadcast image will become
more difficult with printers improvement.
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Rebroadcasting example
Shown is the original iris images (top row) and the images after
being printed and scanned (bottom row).
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Feature vectors projected on 3D space
Results from a four-way classifier of 1000 natural, 1000
steg, 500 graphic, and 200 rebroadcast images.
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More Applications
how many different
artists ?
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More Applications
Forgery detection.
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Finally
• Statistical model.
• capture regularities that are inherent to
photographic images.
• Distinguish tampered \ CG images and
natural images.
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