digital image forensic| copy move forgery

Digital Image Forensics : Copy move forgery

Digital Image ForensicsCopy Move Forgery

Mr Patrick NIYISHAKAReg: 14mcpc21

Supervised by: Prof. Chakravarthy Bhagvati

School Of Computer and InformationScienceHyderabad Central University

December 9, 2016

Patrick NIYISHAKA, PhD Scholar@HCU DRC on December 1,2016 1/25


Previous WorkProblem StatementsProposed SolutionsConclusion and Future Scope

Overview

1 Previous Work.

2 Problem Statements : Challenges to tackle.

3 Proposed Solutions.

4 Conclusion and Future Scope.




Previous Work

1 Introduction to Digital Image Forensics.

2 Image Forgery Types and Their Detection: A Review.

3 Copy Move Attack forgery

4 Comparative study on forgery detection techniques

5 Literature Survey.




Review I. Brief History of Image Tampering

Photography lost itsinnocence many yearsago. Only a few decadesafter Niepce created thefirst photograph in 1814,photographs were alreadybeing manipulated.

Circa 1865




Review II. Brief History of Image tampering




Review III. Copy Move Attack — Definition

In the Copy-Move imagemanipulation technique apart of the sameimage is copied andpasted into anotherpart of that imageitself..




Review IV. Robust Match Detection Technique — Block Diagram




Problem statements I. Challenges To Tackle

1 Computational complexity(Time and space) very high.

2 This kind of attack is not detectable using forensicmethods that look for incompatibilities in statisticalmeasures since The copied parts are from the sameimage,components (e.g., noise, color,..) will becompatible with the rest of the image.

3 Spot which is the original patch,between two copies.

4 Poor performance in detecting small copied regions,(up tonow, attacks on very smooth regions, e.g., depicting thesky, are usually considered false positives).




Problem statements II. Challenges To Tackle.

1 The process of creating fake image has beentremendously simple with the introduction of powerfulcomputer graphics editing software such as AdobePhotoshop, GIMP, and Corel Paint Shop, some of whichare available for free.

2 It is not easy to objectively assess the performance ofthese techniques because, being human assisted, theycannot be tested on massive amounts of data.




Proposed Solution I. Reducing Space Complexity Using CDF.

Figure: Existing Method.Two identical rows in the matrixA correspond to Two identicalBxB blocks.(M—B+1)(N—B+1)rows andB2 Columns.

Figure: Proposed Method.Compute cdf for each BxB blockand store their sum result in anarray.We obtain 1D array,each valuefor each block BXB




CDF : Cumulative Density function — Definition.

1 The distribution functionD(x), also called thecumulative distributionfunction (CDF) orcumulative frequencyfunction, describes theprobability that a variateX takes on a value lessthan or equal to anumber x. Thedistribution function issometimes also denotedF(x)

FX(x)=P(X ≤ x), for all x R.




Benefit I. CDF

Cdf always has positive slopeOne of the main advantages of the CDF is the fact thatmain and important key values and features likeminimum, maximum, median, quantiles, percentiles, etc.can be directly read from the diagram.




Benefit II. CDF

The CDF is much more suitable for comparisons ofseveral data sets . An arbitrary number of CDFs can beplotted into the same axes without any problems forcomparisons. Hereby it is irrelevant how much data eachset actually contains.




Extract windows Which Have Matching CDF Values

Index = Window Number

Duplicate Indexes [ 7, 104]Values [194.078, 194.078]

Duplicate Indexes :[ 0, 10,20, 21]Values : [200.7, 200.7, 200.7, 200.7]




Proposed Solutions II. Robust Detection Using Regression Analysis/Cdf

Regression estimates are used to describe data and toexplain the relationship between one dependent variableand one or more independent variables.




Proposed Solutions II. Linear Regression

Linear regression is the most basicand commonly used predictiveanalysis.




Proposed Solutions II. Block Diagram

Robust Detection Usingproposedmethods—Apply aRegression Analysis onblocks with matching cdfvalues.Finally getmatching slopes andintercepts.




Result : Sample output

Figure: Input Image. Figure: Tampered Image.




Result : Sample output

Figure: Detection withno-overlapping windows.Windows Size 8x8

Figure: Detection with step 4overlapping windows.WindowsSize 8x8




Conclusion

1 Proposed Solutions I.1 With the Cdf proposed method the computing

complexity(space) have been reduced from (M—B+1)(N—B+1)rows and B2 columns matrix to 1D array.

2 Outperforms contemporary algorithms in storage byReducing memory requirements.

2 Proposed Solutions II.1 The detection using linear regression is not robust:

[ Not Working when Image is compressed eg: Jpg][ Not detecting matching for some images]




Future Scope: Extract Matching blocks using cdf features

For blocks with matching Cdfs we extract features :Minimum,Median,Quantile,and Maximum and we useany similarity measure to match.




Questions or Suggestions




References

1 2014-08-11 15:28 by Andreas Kuhn—Why we love the CDFand do not like histograms that much - ANDATA

2 H. Huang, W. Guo, Y. Zhang, Detection of copymove forgeryin digital images using sift algorithm, in: PACIIA 08:Proceedings of the 2008 IEEE Pacific-Asia Workshop onComputational Intelligence and Industrial Application, IEEEComputer Society, Washington, DC, USA, 2008, pp. 272276.

3 T.-T. Ng, S.-F. Chang, C.-Y. Lin, Q. Sun, Passive-blindimage forensics, in: W. Zeng, H. Yu, C.Y. Lin (Eds.),Multimedia Security Technologies for Digital Rights, Elsevier,Hawthorne, NY, USA, 2006.

4 W. Luo, Z. Qu, J. Huang, G. Qiu, A novel method fordetecting cropped and recompressed image block, in: IEEEInternational Conference on Acoustics, Speech and SignalProcessing, vol. 2, Honolulu, HI, USA, April 2007, pp.217220.Patrick NIYISHAKA, PhD Scholar@HCU DRC on December 1,2016 24/25



End

Thank you!


digital image forensic| copy move forgery

Education