An Efficient Image Forensic Mechanism using Super Pixel by SIFT and LFP Algorithm

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2806
An Efficient Image Forensic Mechanism using Super pixel by SIFT and
LFP Algorithm
SHAHABUDDIN.S.K 1, DR. A.R.ASWATHA2
1 Mtech Student, 4th semester, Department of Telecommunication, Dayananda Sagar College of Engineering,
Bangalore -78, Karnataka,India
2Professor and Head, Department of Telecommunication, Dayananda Sagar College of Engineering,
Bangalore-78, Karnataka,India
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Abstract—Image forgery is a very significant and challengingarea inthefieldofimageprocessinganditsapplications.Oneof
the very commonly used image forgery technique is the copy-move technique which isnotonlya commontechniquebutalsoa
challenging technique. Initially a wavelet decomposition methodisappliedinorderto computethesizeofsuper pixelswhich is
further applied to segmentation block. Post segmentation the feature extraction and feature matching is performed using
suitable methods. Consequently region growing is performed in order to detect the forged area in theimage.Finallytheregion
of forgery is detected and evaluated using the measures of precision, sensitivity, specificity and F1 score.Experimental results
show an increased performance in the above defined measures.
Key-Words: Image Forgery, Copy-Move Method, Wavelet Decomposition, Segmentation, Region Growing
1. Introduction
In the present day of image processing applications, the sophistication and complexity of images has increased to a
considerable extent, which provides a broader scope in variety of areas ranging from military applications to biomedical
applications. However, the increase in altering (editing) the image has linearly increased, withthecommercializationofimage
editing tools, the issue has only deepened to the extent where the credibility and authenticityoftheimageisquestionable. One
practical application highlighting this issue involves the editing of photography in a crimescene;theaggregatorcouldalterthe
content in the image deliberately in order to hide the key evidence which might otherwise be visible in the respective image.
Image forgery is defined as a manipulation of digital image in order to conceal or alter the meaningful or useful information
present in the image. The first person to have successfully altered the image was in 1840 by a man named Hippolyta Bayard
who edited a picture of himself.
However, the extent of digital forgery in the image does not differ much from its original image which makes it difficult for the
interpreter to identify the element of forgery in the edited image.
The Copy-Move technique is considered as one of the most commonly used image tamperingtechnique.Itisalsoconsideredas
one of the most difficult technique. This is due to the factor that the manipulation of image content is within the imageitself.In
this technique, a part of the image is deliberately covered by copying a part of the image and pasting(Moving/superimposing)
it in another place in the same image. The intention of implementing this technique is to alter the image by hiding a portion/
part of an image.
Tu and Dong [11] have significantly explained the image featurematchingandalsothehistogramequalizationmechanism. The
algorithm implemented for features matching of an image will provide the better efficiency with the image scale, rotation,
illumination difference for practical applications. The image acquisition process includes various conditions like posture,
position and camera performance etc., for the image area. The projective distortion of theimagearea will affecttheaccuracyin
FP matching/extraction. The author has given the preprocessingmechanismto enhancethesignificantfeaturesoftheimage by
using the histogram equalization. Later the image is subjected to ASIFT and SIFTS algorithms for matchingand extraction.The
experiment conducted by the author gives the improved way to get more number of feature points by histogram equalization.
The color spaces evaluation to detect the feature point of image for the matching application is explained in Sirisha and
Sandhya [12]. The study of the authors includes the inspection of the color information according to the FP detection. These
color information’s are expressed as HSV, XYZ, RGB, LAB, YIQ, CMY, CbCr andopponent. Latermorerelevantcolorspacewill be
problem and hence the processing of color image is done. The image matching is done with the Hybrid color space while the
Principal Component Analysis (PCA) is used for feature selection. The experiment is conducted by keeping the total
repeatability measurement and total feature points for evaluation.

Wang and Wang [13] have illustrated an algorithm for background image feature point matching. Author has focused on the
issues of the matching the feature points of an complex background image having weak robustness and low reliability. The
authors have carried multiple time real time experimental analysisovertheimage byusingSURFandSIFTalgorithmandhence
concluded that,
 Both the algorithms are effective for rotation change, noise, light and other integrated point of concerns.
 The synthesis analysis says that SURF algorithm has efficiency than SIFT algorithm.
 For faster, accurate and robust match SURF algorithm is preferable.
The paper is structured as follows, the first section gives a brief introduction of image forgery and its related works, The
second section gives a brief review of literature in the field of imageforgery,Thethird section explainstheimplementation
of the proposed work through a general system architecture. The fourth section shows the simulation results and the
results evaluation with respect to proposed system. The last section gives a brief conclusion of the overall proposed
system.
2.Literature Review
Author Math et al. [1] have discussed the different kinds of digital forgeries and also the existing challenges/issues in it. The
author has mentioned that the advancement in the software trend, variety of capturing tools, processing tools, accessingtools
and transmitting tools of digital information, which has made toughest task to identify the tampering by visually or by other
processes. The latest research domain for the digital forgery has gained its popularity due to the existing problem complexity.
The author’s paper satisfactorily concluded the problems seriousness impacting for the future research.
The LU decomposition and cellular automata based forgery detection of gray scale image is illustratedby presentingtheactive
algorithm in Far et al. [2]. Authors paper has mentioned that the detecting the image forgery is a still open challenge for
research in image processing area. Since last few years, many of the authors have expanded their study to identify the image
forgery according to the advancement image forgery techniques. The image forgery detection algorithms can be classified as
two categories active and passive. The active approach: followed with the creation and embedding of invaluable data and will
be considered as cipher key, which helps in protecting the real image againsttheforgery.The passiveapproachwill befollowed
with the investigation of image features, compressions and correlations for forgery detection. The author has suggestedsome
important aspects for future study. Similar study is explainedinNodeh[3]andhasintroducedtheactiveapproachbasedimage
forgery detection.
Musthtaq et al. [4] surveyed the image forgeries and authentication techniques based on passive approaches. The author has
expressed the existing issues of image forgery, where the advanced image manipulation tools are used by which the detection
becomes very complicated. The existing research towards the forgery detection is reviewed and state of art suggests that the
passive methods can be used for digital image authentication.
AL-Quershi et al. [5] have presented the passive approach to detect the copy move typeofimageforgery.Authorhasexpressed
that the passive approach can be used most in image forensics. In this copy move based image forgery is focused for the
research, where the some part of the image will be copied and moved to another place of the same image. According to the
active approach of water marking method was used to overcome the authenticity issue, but the problemwiththis approachis
that it requires specially equipped camera or the human intervention. In order to overcome these kind of issues the research
institutes and researchers are proposed the passive approach, whichdoesn’trequiresspeciallyequippedcamera orthehuman
intervention. The authors study helps in understanding the copy move image forgery detections state of art, key points
required to develop the proper detector are addressed along with the issues and solutions for those issue.
Alahmadi et al. [6] have addressed the local binary pattern and Discrete Cosine Transform (DCT) based passive approach for
image copy move based forgery detection. The approach is implemented as follow.
 The discriminative features and chrominance features are extracted by the application of two dimensional Discrete
Cosine Transform (2DDCT) in local binary pattern space.
 For detection purpose support vector machine (SVM) is implemented. The experiment for the detection was carried
out which outcomes with the superior results with accuracy than the existing methods.
Hashima et al. [7] have presented the combined (SIFT and DyWT) algorithm method for detecting the copy paste based image
forgery. These two algorithms are implemented for following purpose:
 Dyadic Wavelet Transform (DyWT): To decompose the image as Low-low, Low-High, high-low and high-high parts.
 Scale Invariant Feature Transform (SIFT): To extract the key features exist in Low-Low part of the imageandidentify
the identity among these key features. The presented method was efficient in identification aspects.

Maind et al. [8] have given the block representation method to identify the copy move based image forgery. The method is
implemented by dividing the image into fixed block sized parts and subjecting the DCT (represents every block). The blocks
transformed by DCT will be forwarded and represented as circle block and the image dimension is reduced by extracting the
four features. Later these features will be sorted and matched with threshold value of the image block. The wrongblockswere
eliminated to make efficient algorithm. The method was experimentally successfully helps in detecting the copy move based
forgery and also helps in noise adding or blurring within less computational complexity.
Kasyap and Joshi [9] have given the wavelet decomposition based copy move image forgery detection. The approach has
achieved the low computation time and 87% accurate detection schemeofdetection.InDespande etal.[10]havediscussedthe
image forgery detection techniques based on its pixel. The author has described the two kinds of image pixel based forgery
detection scheme along with the copy move forgery detection (deals with only shifting of copied area). These techniques are
compared with the fast copy move technique by analysis and suggested the future work flow.
3.Implmentation
The general system architecture of the proposed system is shown in fig 1.The decompositionoftheimagedata isperformed by
applying discrete wavelet transformation (DWT) using Haar wavelet as the basis function (mother wavelet). The operationof
decomposition is performed post angle rotation of the image. The decomposition results in two coefficients which are thelow
frequency approximation coefficients and the high frequency detail coefficients. Using the low frequency and the high
frequency components the size for the super pixels is computed.
Squared special distance is computed for the decomposed image coefficients. The difference between the squared color
distances is then computed after which a combination of colordistanceandspecial distanceismeasured.Thesegmentedimage
is finally obtained by combining edge distance and its weight along with total distance. Clustering based method is used in the
SLIC algorithm which provides a tradeoff between color similarity and proximity with respect to compactness parameter
Discrete Wavelet
decomposition
AC DC
Rotate
Eq.1
Eq.2
Eq.3
Eq.4
SLIC
segmentation
SIFT
algorithm
Identify
blob
Store
features
Compute
distance
vector
Set
threshold
for d/s >0
Find most
repeating
distance
Define window size
Extract region from defined
window size
Compute average at third
dimension
Find absolute difference
Obtain one highest region
Region 1 Region 2
Store final output in the
respective position
Decomposition process Segmentation
Feature extraction and feature matching (LFP
method)
Region growing

The SIFT algorithm is used in the process of feature extraction in the proposed system. This algorithm mainly consists of five
important stages. In the first stage the feature detection is performed whichisscaleinvariant(whichisalsoknownasdilation).
The features are matched and respective labels are mentioned (which is used for the purpose of indexing). Consequently
clustering based method is used for identification of feature and the Hough transformation is used for the purpose of voting.
The model is verified by applying the least square method and finally the outlier is detected.
4.Simulation Results
The database considered in this project is a set of images of bmp and .png format. The images are of type double with intensity
level representation of 28 bits range, i.e. the intensity levels in the image ranges from 0 to 255. The above database with the
following attributes mentioned in table 1. The simulation results are shown in table 2,3,4 and 5.
Table 1: Database for the implementation of project
Table 2: Input image and ground truth
input 1 input 2
input 3
ground truth image 1 ( input 4) ground truth image 2(input 5) ground truth image 3 (input 6)
Sl.no Input Image format Image size Bytes Class
1 Input1 .png 512x512x3 786432 uint8
2 Input2 .png 600x800 480000 uint8
3 Input3 .png 532x800 425600 uint8
4 Input4 .bmp 512x512x24 786486 uint8
5 Input5 .bmp 600x800x3 1440000 uint8
6 Input6 bmp 532x800x3 1276800 uint8

Table 3: Feature extraction using SIFT algorithm
Feature extraction using SIFT algorithm
input 1
input 2
input 3
Table 4: Feature matching using LFP method
Feature matching using LFP method
Feature detection LFP method
LFP
LFP
LFP
Detected sift features Detected sift features
Detected sift features

Table 5: Detected forgery region
Detected forgery image
input 1
input 2
input 3
5.Result Analysis The evaluation performed with respect to image forgery detection is mentioned as follows,
1. Precision: The precision for the detection of forgery is considered as one of the factors in evaluation. Theprecisionis
defined as the ratio of true positive to sum of true positive and false positive. It is mathematically defined as shown in eq. 1,
……………….. (1)
Where,  true positive, given as
Where, bw1 binary image (i.e. region growing)
GT  ground truth
bwGT  extracted area from ground truth
 False positive
…………. (2)
2. Sensitivity: Sensitivity is defined as the ratio of true positive to sum of true positive and false negative. It is
mathematically defined as shown in eq.3,
……………… (3)
GT Detected region
GT Detected region
GT
Detected region

Where, FN  False negative, given as shown in eq.4
……………. (4)
3. Specificity: It is defined as the ratio of true negative to sum of true negative and false positive. It is mathematically
represented as shown in eq.5,
……………………… (5)
Where,  true negative, given as shown in eq.6
………….. (6)
4. F1 score: It is defined as twice the ratio of product of precision and sensitivity to sum of precision and sensitivity.Itis
mathematically defined as shown in eq.7,
……. (7)
The simulation results for the above evaluation is given as follows in table 6,
Table 6: Parametric evaluations with respect to forgery detection
Parameters image 1 image 2 image 3
Elapsed time 33.82 s 74.02 s 277.70
total number of key points 1886 5810 6363
total matches 154 254 351
TP 0.8401 0.9453 0.8455
TN 0.9956 0.9989 0.9980
FP 0.0042 0.0010 0.0018
FN 0.1563 0.0517 0.1536
Precision 0.9949 0.9989 0.9978
Sensitivity 0.8430 0.9481 0.8461
Specificity 0.9957 0.9989 0.9978
F1 score 0.9127 0.9728 0.9157
6. Conclusion
In this project the copy-move forgery detectionisconsideredwhichisthemostcommonandsignificanttypeofforgerymethod.
Post wavelet decomposition, a feature extraction is performed using SIFT algorithm and the feature matching is performed
using LFP. The region growing is performed finally to obtain the region where the image is forged. The evaluationsconsidered
for the proposed system are precision, sensitivity,specificityandF1score.Experimental resultsshowthattheproposed system
had better efficiency and performance with respect to above evaluations.

References
[1] Math, Shrishail, and R. C. Tripathi. "Digital Forgeries: Problems and Challenges." International Journal of Computer
Applications 5.12 (2010): 9-12.
[2] Far, Mohammad Amin Moghaddasi, Faezeh Rohani, andE.Brhravesh."AnActiveAlgorithmtoGray-scaleDigital Image
Forgery Detection based on Cellular Automata and LU Decomposition." J Comput Sci Softw Dev 1.001 (2015).
[3] Nodeh, Maryam Pahlavan. "Active Image Forgery Detection: State of the Art and Possible Enhancements." J. Elec.
Commu. Eng. Resol 1.1 (2016): 11-13.
[4] Mushtaq, Saba, and Ajaz Hussain Mir. "Digital Image Forgeries and Passive Image Authentication Techniques: A
Survey." International Journal of Advanced Science and Technology 73 (2014): 15-32.
[5] Al-Qershi, Osamah M., and Khoo Bee Ee. "Passive Detection of Copy-Move Forgery in Digital Images:State-of-the-art."
[6] Amani Alahmadi, Muhammad Hussain1,a, Hatim Aboalsamh , Ghulam Muhammad , GeorgeBebis,HassanMathkour,"
Passive Detection of Image Forgery using DCT and LocalBinary Pattern".
[7] Mohammad Farukh Hashmia,VijayAnandb,AvinasG.Keskarc,"Copy-moveImageForgeryDetectionUsinganEfficient
and Robust Method Combining Un-decimated WaveletTransform and Scale Invariant Feature Transform, "AASRI
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[8] Rohini.A.Maind, Alka Khade, D.K.Chitre , "Image Copy Move Forgery Detection using Block Representing Method ",
International Journal of Soft Computing and Engineering (IJSCE) pp.2231-2307, Volume-4, Issue-2, May 2014.
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[10] Deshpande, Pradyumna, and Prashasti Kanikar. "Pixel based digital image forgery detection techniques."
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[11] L. Tu and C. Dong, "Histogram equalization and image feature matching," ImageandSignal Processing(CISP),
2013 6th International Congress on, Hangzhou, 2013, pp. 443-447.
[12] B. Sirisha and B. Sandhya, "Evaluation of Color Spaces for Feature Point Detection in Image Matching
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2013, pp. 216-219.
[13] X. Wang, D. Liu and L. Wang, "A Feature Point Matching Algorithm for Complex Background Image," Big Data
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