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

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

An Example of Image Region Duplication

Original image Tampered image

Outline

– Motivation

– Related Works

– Detection Method

– Experimental Results

– Discussion and Future Work

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

Outline

– Motivation

– Related Works

– Detection Method

– Experimental Result

– Discussion and future work

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

Simple copy-move

Original Image Image tampered using copy-move

Outline

– Motivation

– Related Works

– Detection Method

– Experimental Results

– Discussion and Future Work

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

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

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

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

SIFT Keypoint Matching and Pruning (cont.)

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

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

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

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

Obtaining Duplication Map• A perspective image is generated from the original image

using the estimated parameters

Tampered Image Perspective Image

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

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

Obtaining Duplication Map (cont.)

Outline

– Motivation

– Related Works

– Detection Method

– Experimental Results

– Discussion and future Work

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)

PDA and PFP

T

S

T~

S~||

|~||~|PDATS

TTSS

|~~||~||~|PFP

TS

TTSS

Copy-move

PDA = 91.3%, PFP = 0.3%Tampered image using copy-move

Scaling

PDA = 93.4%, PFP = 1.2%Tampered image using scaling factor: 1.2

Rotation

PDA = 81.4%, PFP = 1.9%Tampered image using rotation: 60⁰

Qualitative Testing Results (cont.)

Possible duplicated regions confirmed by inspection of photography experts

Detected duplicated region using our method

Qualitative Testing Results (cont.)

Forgeries generated with using the Smart Fill tool [Alien Skin Software LLC]

Video Forgery

Detection of Video Forgery

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

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.

Outline

– Motivation

– Related Works

– Detection Method

– Experimental Results

– Discussion and Future work

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

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.

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)

Thank You!

Questions?