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GEOMETRIC TAMPERING ESTIMATION  BY MEANS OF A SIFT-BASED FORENSIC ANALYSIS Irene Amerini, Lamberto Ballan,  Roberto Caldel...
Summary <ul><li>Image forensics: the copy-move attack </li></ul><ul><li>The SIFT technique </li></ul><ul><li>The proposed ...
The copy-move attack <ul><li>One of the main purposes of  Image Forensics  is  to basically assess the authenticity of an ...
The copy-move attack
The copy-move attack
Copy-move & SIFT  <ul><li>In object detection and recognition, techniques based on scene modeling through a collection of ...
SIFT <ul><li>SIFT features are detected at different scales by using a scale space representation implemented as an image ...
SIFT <ul><li>Once such  keypoints  are detected, SIFT descriptors are computed at their locations in both image plane and ...
The proposed approach Due to their invariance SIFT features are well-suited to detect forgeries through a matching operati...
Matching among keypoints <ul><li>The keypoints X={x 1 ,..,x N } are extracted with a SIFT descriptor associated </li></ul>...
Hierarchical clustering (1/2) <ul><li>Agglomerative Hierarchical Clustering, based on spatial locations of matched keypoin...
Hierarchical clustering (2/2) <ul><li>Clustering is stopped by evaluating the  inconsistency coefficient ( IC) with respec...
Geometric transformation estimation <ul><li>Clusters which do not contain a significant number of matched keypoints are el...
Geometric transformation estimation <ul><li>A  contains rotation and scale parameters which can be determined by a Single ...
Experimental results:  forgery detection
Experimental results:  forgery detection
Experimental results:  forgery detection
Experimental results:  forgery detection
Experimental results:  forgery detection
Experimental results:  transformation estimation Translation tx tx^ ty ty^ 304 304.02 80.5 81.01 θ θ^ 0 0.040 Rotation  (n...
Experimental results:  transformation estimation tx tx^ ty ty^ 304 305.02 80.5 80.82 Translation θ θ^ 20 20.067 Rotation s...
Experimental results:  transformation estimation
Experimental results:  multiple cloning
Conclusions <ul><li>Copy-move attack is detected by means of a SIFT-based algorithm. </li></ul><ul><li>Geometric transform...
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GEOMETRIC TAMPERING ESTIMATION BY MEANS OF A SIFT-BASED FORENSIC ANALYSIS

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GEOMETRIC TAMPERING ESTIMATION
BY MEANS OF
A SIFT-BASED FORENSIC ANALYSIS
Roberto Caldelli

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GEOMETRIC TAMPERING ESTIMATION BY MEANS OF A SIFT-BASED FORENSIC ANALYSIS

  1. 1. GEOMETRIC TAMPERING ESTIMATION BY MEANS OF A SIFT-BASED FORENSIC ANALYSIS Irene Amerini, Lamberto Ballan, Roberto Caldelli , Alberto Del Bimbo and Giuseppe Serra MICC - Media Integration and Communication Center University of Florence, Florence, Italy
  2. 2. Summary <ul><li>Image forensics: the copy-move attack </li></ul><ul><li>The SIFT technique </li></ul><ul><li>The proposed approach </li></ul><ul><ul><li>Matching </li></ul></ul><ul><ul><li>Clustering </li></ul></ul><ul><ul><li>Geometric transformation estimation </li></ul></ul><ul><li>Experimental results </li></ul><ul><ul><li>Forgery detection </li></ul></ul><ul><ul><li>Transformation parameters estimation </li></ul></ul><ul><li>Conclusions </li></ul>
  3. 3. The copy-move attack <ul><li>One of the main purposes of Image Forensics is to basically assess the authenticity of an image. </li></ul><ul><li>Different kinds of tampering can be performed by an attacker. </li></ul><ul><li>Copy-Move attack : a feigned image is created by cloning an area of the image onto another zone to make a duplication or to cancel something awkward. </li></ul>
  4. 4. The copy-move attack
  5. 5. The copy-move attack
  6. 6. Copy-move & SIFT <ul><li>In object detection and recognition, techniques based on scene modeling through a collection of salient points are often used. </li></ul><ul><li>SIFT ( Scale Invariant Features Transform ) are usually adopted for their high performances and low complexity. </li></ul>TARGET : Forensic analysis should provide instruments to detect such a cloning and to estimate which transformation has been performed.
  7. 7. SIFT <ul><li>SIFT features are detected at different scales by using a scale space representation implemented as an image pyramid. </li></ul><ul><li>The pyramid levels are obtained by Gaussian smoothing and image sub-sampling while keypoints are selected as local extrema (min/max) in the scale space. </li></ul><ul><li>Such keypoints are extracted by iteratively computing the difference between two nearby scales in the scale-space (Difference of Gaussians - DoG ). </li></ul>original image L(x,y,σ) D(x,y,σ) Gaussians DoG Gaussian filtering G(x,y,σ) grey-scale I(x,y)
  8. 8. SIFT <ul><li>Once such keypoints are detected, SIFT descriptors are computed at their locations in both image plane and scale-space. Each SIFT descriptor O consists in a histogram of 128 elements, obtained from a 16x16 pixels area around the corresponding keypoint . </li></ul><ul><li>The contribution of each pixel is obtained by calculating the image gradient magnitude and direction in scale-space and the histogram is computed as the local statistics of gradient directions (8 bins) in 4x4 sub-patches of the 16x16 area. </li></ul><ul><li>Finally each keypoint has a SIFT descriptor associated with it . </li></ul>[2] Lowe. “Distinctive image features from scale-invariant keypoints” Int.’l Journal of Computer Vision, 2004
  9. 9. The proposed approach Due to their invariance SIFT features are well-suited to detect forgeries through a matching operation. Suspected image I Features extraction and matching Geometric transformation estimation Hierarchical clustering H
  10. 10. Matching among keypoints <ul><li>The keypoints X={x 1 ,..,x N } are extracted with a SIFT descriptor associated </li></ul><ul><li>A similarity vector S ={d 1 ,….., d N-1 } which represents the sorted euclidean distance in the SIFT space is computed for each keypoint. </li></ul><ul><li>Two keypoints are then matched if the ratio d 1 /d 2 < T (pre-defined) . </li></ul><ul><li>All matched keypoints are held; isolated ones are discarded. </li></ul>
  11. 11. Hierarchical clustering (1/2) <ul><li>Agglomerative Hierarchical Clustering, based on spatial locations of matched keypoints, is adopted. </li></ul><ul><li>Hierarchical clustering can be represented as a tree structure. </li></ul><ul><li>It starts by assigning each keypoint to a cluster, then it computes all the reciprocal spatial distances among clusters. </li></ul><ul><li>The two clusters with the minimum distance are merged. </li></ul>Criterion: the shortest distance among members belonging to the two different clusters! C 1 C 2 C N-1 C N …… .. C 1,2 C 1,2,8 C 1,2,8, … C N-1,N
  12. 12. Hierarchical clustering (2/2) <ul><li>Clustering is stopped by evaluating the inconsistency coefficient ( IC) with respect to a threshold; </li></ul><ul><li>IC takes basically into account the average distance among clusters and does not allow to join clusters spatially too far at that level of hierarchy. </li></ul>
  13. 13. Geometric transformation estimation <ul><li>Clusters which do not contain a significant number of matched keypoints are eliminated. </li></ul><ul><li>Remained clusters are considered and their keypoints are used to estimate matrix H (homography) which moves one cluster into another one. </li></ul><ul><li>Estimation is performed through RANSAC (RANdom SAmple Consensus) algorithm which permits to improve results by reducing the disturbing effect of outliers. </li></ul>
  14. 14. Geometric transformation estimation <ul><li>A contains rotation and scale parameters which can be determined by a Single Value Decomposition (SVD). </li></ul>Translation parameters are determined by using clusters’ centroids Rotation and scale parameters H
  15. 15. Experimental results: forgery detection
  16. 16. Experimental results: forgery detection
  17. 17. Experimental results: forgery detection
  18. 18. Experimental results: forgery detection
  19. 19. Experimental results: forgery detection
  20. 20. Experimental results: transformation estimation Translation tx tx^ ty ty^ 304 304.02 80.5 81.01 θ θ^ 0 0.040 Rotation (no rotation) sx sx^ sy sy^ 1 1.004 1 0.998 Scaling (no scaling)
  21. 21. Experimental results: transformation estimation tx tx^ ty ty^ 304 305.02 80.5 80.82 Translation θ θ^ 20 20.067 Rotation sx sx^ sy sy^ 1.4 1.404 1.2 1.198 Scaling
  22. 22. Experimental results: transformation estimation
  23. 23. Experimental results: multiple cloning
  24. 24. Conclusions <ul><li>Copy-move attack is detected by means of a SIFT-based algorithm. </li></ul><ul><li>Geometric transformation parameters are estimated. </li></ul><ul><li>Such a technique has to be improved in relation with the size and the texture of the cloned patch. </li></ul><ul><li>It could be applied against splicing attack when a suspected source image set is available. </li></ul>

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