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Detecting Image Region Duplication Using SIFT Features
March 16, ICASSP 2010Dallas, TX
Xunyu Pan and Siwei LyuComputer Science Department
University at AlbanyState University of New York
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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
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An Example of Image Region Duplication
Original image Tampered image
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Outline
– Motivation
– Related Works
– Detection Method
– Experimental Results
– Discussion and Future Work
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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
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Outline
– Motivation
– Related Works
– Detection Method
– Experimental Result
– Discussion and future work
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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
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Simple copy-move
Original Image Image tampered using copy-move
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Outline
– Motivation
– Related Works
– Detection Method
– Experimental Results
– Discussion and Future Work
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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
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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
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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
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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
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SIFT Keypoint Matching and Pruning (cont.)
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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:
),( yx
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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
ScaleFactor
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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
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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
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Obtaining Duplication Map• A perspective image is generated from the original image
using the estimated parameters
Tampered Image Perspective Image
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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
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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
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Obtaining Duplication Map (cont.)
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Outline
– Motivation
– Related Works
– Detection Method
– Experimental Results
– Discussion and future Work
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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)
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PDA and PFP
T
S
T~
S~||
|~||~|PDATS
TTSS
|~~||~||~|PFP
TS
TTSS
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Copy-move
PDA = 91.3%, PFP = 0.3%Tampered image using copy-move
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Scaling
PDA = 93.4%, PFP = 1.2%Tampered image using scaling factor: 1.2
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Rotation
PDA = 81.4%, PFP = 1.9%Tampered image using rotation: 60⁰
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Qualitative Testing Results (cont.)
Possible duplicated regions confirmed by inspection of photography experts
Detected duplicated region using our method
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Qualitative Testing Results (cont.)
Forgeries generated with using the Smart Fill tool [Alien Skin Software LLC]
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Video Forgery
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Detection of Video Forgery
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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
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Robustness and Sensitivity on JPEG and Noise
JPEG Q = 60 Q = 70 Q = 80 Q = 90 Q = 10064 × 64 86.23/1.74 85.04/1.66 89.79/1.68 90.49/1.75 92.76/2.1596 × 96 91.42/0.93 92.35/0.96 93.02/0.95 93.85/1.02 95.05/1.20
SNR 20 dB 25 dB 30 dB 35 dB 40 dB64 × 64 89.76/1.78 92.06/2.03 92.43/2.08 92.55/2.07 92.61/2.1396 × 96 92.84/1.02 94.24/1.13 94.62/1.18 94.70/1.18 94.78/1.19
Average detection accuracies and false positives (both in percentage) on100 testing images.
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Outline
– Motivation
– Related Works
– Detection Method
– Experimental Results
– Discussion and Future work
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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
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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.
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Future Work
• Convert the algorithm into a plug-in for Photoshop, so that it can become part of the image forensic examiner’s toolkit
• Detecting forged image created by duplicated regions from original image with statistical similarity (e.g., texture synthesis)
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Thank You!
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Questions?