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

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 imageTampered 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 ImageImage 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:

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

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

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 JPEGQ = 60Q = 70Q = 80Q = 90Q = × / / / / / × / / / / /1.20 SNR20 dB25 dB30 dB35 dB40 dB 64 × / / / / / × / / / / /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!

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