Detecting Image Region Duplication Using SIFT Features March
16, ICASSP 2010 Dallas, TX Xunyu Pan and Siwei Lyu Computer Science
Department University at Albany State University of New York
Slide 2
An Example of Image Region Duplication Forgeries appeared on
the front page of The Los Angeles Times, The Financial Times, The
Chicago Tribune and The New York Times
Slide 3
An Example of Image Region Duplication Original imageTampered
image
Slide 4
Outline Motivation Related Works Detection Method Experimental
Results Discussion and Future Work
Slide 5
Motivation Region duplication is a common manipulation used in
image tampering Most existing methods based on finding exact copies
of pixel blocks They are not effective for geometric and
illumination adjustments over the regions We propose a new method
to detect such more complicated region duplication
Slide 6
Outline Motivation Related Works Detection Method Experimental
Result Discussion and future work
Slide 7
Related Works Majority of previous works focus on detecting
copy-move forgery problems The methods used are based on comparing
pixel blocks (exhaustive search) Most methods reduce computation by
using low dimensional representations of blocks [Popescu and
Farid,04]and [Luo, et al.,06] use PCA to reduce computation
[Fridrich, et al.,03] uses DCT to reduce computation [Huang, et
al.,08] uses invariant image features to detect copy- move region
duplication
Slide 8
Simple copy-move Original ImageImage tampered using
copy-move
Slide 9
Outline Motivation Related Works Detection Method Experimental
Results Discussion and Future Work
Slide 10
Major Steps of the Proposed Method STEP 1: Scale Invariant
Feature Transform (SIFT) keypoint detection STEP2: SIFT keypoint
matching and manipulation transform estimation STEP3: Obtaining
duplication map
Slide 11
SIFT Keypoint SIFT (Scale Invariant Feature Transform) was
originally proposed by [Lowe, 99] and further optimized in [Lowe,
04] SIFT keypoints detected on a tampered image
Slide 12
Major Steps of the Proposed Method STEP 1: Scale Invariant
Feature Transform (SIFT) keypoint detection STEP2: SIFT keypoint
matching and manipulation transform estimation STEP3: Obtaining
duplication map
Slide 13
Keypoints Matching Best-Bin-First (BBF) algorithm [Beis, 97] is
used to match similar keypoints Selection criteria: The best match
f of keypoint f should be far more close (Euclidean distance) than
all the other matches
Slide 14
SIFT Keypoint Matching and Pruning (cont.)
Slide 15
Estimation of Manipulation Transform Copy-move Compute
Euclidean distance for each keypoint correspondence Shift vector is
estimated by the distance with maximum frequency of occurrence
Shift Vector:
Slide 16
Estimation of Manipulation Transform Scaling Compute ratio of
Euclidean distance between corresponding keypoint pairs The ratio
with the maximum frequency is used as an estimation of the scale
factor Scale Factor
Slide 17
Estimation of Manipulation Transform Rotation Keypoint
represented by a local coordinate systems consists of three
non-collinear keypoints Transform is estimated based on the same
set of coordinates in the coordinate system Local Coordinate
System
Slide 18
Major Steps of the Proposed Method STEP 1: Scale Invariant
Feature Transform (SIFT) keypoint detection STEP2: SIFT keypoint
matching and manipulation transform estimation STEP3: Obtaining
duplication map
Slide 19
Obtaining Duplication Map A perspective image is generated from
the original image using the estimated parameters Tampered Image
Perspective Image
Slide 20
Obtaining Duplication Map (cont.) Both images are then
segmented into overlapping 4 4 pixels contour blocks A correlation
map can be generated by computing the correlation coefficient
between each pair of corresponding contour blocks in original and
perspective image respectively
Slide 21
Obtaining Duplication Map (cont.) Apply 7 7 Gaussian filter to
smooth the correlation map Binarize the correlation map using a
preset threshold Remove small isolated regions caused by noise
using a pre-given area threshold The final contour is connected
using mathematical morphological operations [Suzuki, 85] Erosion
and Dilation
Slide 22
Obtaining Duplication Map (cont.)
Slide 23
Outline Motivation Related Works Detection Method Experimental
Results Discussion and future Work
Slide 24
Qualitative Testing Results We define two quantitative measure
of performance based on the detection accuracy and false positives
Pixel detection accuracy (PDA) Pixel false positive (PFP)
Slide 25
PDA and PFP
Slide 26
Copy-move PDA = 91.3%, PFP = 0.3%Tampered image using
copy-move
Qualitative Testing Results (cont.) Possible duplicated regions
confirmed by inspection of photography experts Detected duplicated
region using our method
Slide 30
Qualitative Testing Results (cont.) Forgeries generated with
using the Smart Fill tool [Alien Skin Software LLC]
Slide 31
Video Forgery
Slide 32
Detection of Video Forgery
Slide 33
Quantitative Testing Results An image database of tampered
color images of 720 436 pixels which are originally captured by
Nikon D100 digital camera In each image, a random square region of
size 64 64 pixels (1.3% of the original image) or 96 96 pixels was
copied and pasted onto a random position in the same image
Slide 34
Robustness and Sensitivity on JPEG and Noise JPEGQ = 60Q = 70Q
= 80Q = 90Q = 100 64
6486.23/1.7485.04/1.6689.79/1.6890.49/1.7592.76/2.15 96
9691.42/0.9392.35/0.9693.02/0.9593.85/1.0295.05/1.20 SNR20 dB25
dB30 dB35 dB40 dB 64
6489.76/1.7892.06/2.0392.43/2.0892.55/2.0792.61/2.13 96
9692.84/1.0294.24/1.1394.62/1.1894.70/1.1894.78/1.19 Average
detection accuracies and false positives (both in percentage) on100
testing images.
Slide 35
Outline Motivation Related Works Detection Method Experimental
Results Discussion and Future work
Slide 36
Summary In this work, we propose an efficient technique to
detect region duplication in digital images based on the matching
of SIFT features Compared to previous methods, our method is
effective to the detection of duplicated regions undergone
geometric and illumination adjustments Experimental results with
several credible forgeries demonstrate the efficacy of our
method
Slide 37
Discussion Advantages Reliably detect duplicated regions that
are geometrically distorted Robust to general image degradations
caused by JPEG compression or additive noise Efficient running time
analyzing images of normal size takes about 10 seconds on a
computer of Intel 2.0GHz processor with 2 GB memory Limitations The
specific manipulation transform must be known before the detection,
which is usually impracticable Detection performance reduces as the
duplicated regions become smaller or homogeneous (e.g. sky) Certain
distortion of the duplicated region (e.g., extreme scaling or
reflection) can affect the invariance of the SIFT features.
Slide 38
Future Work Convert the algorithm into a plug-in for Photoshop,
so that it can become part of the image forensic examiners toolkit
Detecting forged image created by duplicated regions from original
image with statistical similarity (e.g., texture synthesis)
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