STUDY OF THE IMPORTANCE OF DIGITAL FORENSICS AND DEEP LEARNING TOOLS Arun Anoop M, Dr.S.Poonkuntran, Dr.V.Vasudevan, Dr.P.Alli

(1)

963

STUDY OF THE IMPORTANCE OF DIGITAL FORENSICS AND DEEP LEARNING TOOLS

Arun Anoop M, Dr.S.Poonkuntran, Dr.V.Vasudevan, Dr.P.Alli

1 M, PhD Scholar, Department of Computer Science and Engineering, Velammal College of Engineering and Technology(Under Anna University), Viraganoor, Madurai,Tamil Nadu,India

2,Professor, Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Viraganoor, Madurai,Tamil Nadu,India

3, Professor, Dept. of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India

4, Professor, Dept. of Computer science and Engineering, Velammal College of Engineering and Technology, Viraganoor, Madurai,Tamil Nadu,India

Abstract:

In today’s advanced age the reliable towards picture is twisting a direct result of malicious forgery images. The issues identified with the security have prompted the examination center towards tampering detection. As the source image also, the objective locales are from a similar picture so that copy move forgery is very effective in image manipulation due to its same properties such as temperature, color, noise and illumination conditions. Resizing images, changing brightness or applying some image transformations, copying someone's head and pasting into other's body, bill forgery also a kind of digital image forgery. Digital forensics tools in the sense, it`s normally image, audio, video or VoIP based areas.

And moreover many methods used for the detection of digital forensics especially machine learning and deep learning. In machine learning, there are supervised and unsupervised methods are the common.

There are many deep learning methods available for analysis, compared some deep learning based tools, concepts regarding image forgery block based methods and machine learning algorithms.

Keywords: Image Forgery and its detection, Optimization methods, Deep learning, digital image forensics, machine learning.

INTRODUCTION

Digital images are an emerging innovation in the field of information source. But in nowadays many illegal activities are occurred. Digital image are used in some of the field of commercial for an example medical, astronomy, law enforcement and photorealism. The requirement of digital imaging in the modern field to improve the quality of an image to the viewer, elaborate the image significant data which is not visible to the eye, and balance an image by means of photometric or geometric.

Attacks like man in the middle attack and hijacking will falsify the sharing information mainly images, voice calls and texts. These vulnerabilities leads to image manipulations for getting 3^rd parties advantages.

In the around 1865, understood picture taker Mathew Brady, general Sherman is recognizable posturing together with his Generals, General Francis P. Blair ( far right) was once familiar with the typical picture. Copy move forgery is one of the particular form of image tampering where a piece of the image is copy-pasted on other piece of the same image. Digital image forgery are happen due to leading editing software and cameras. Because there has been some work in digital signature and watermarking to identify and place image manipulation.

Computation of embedding watermark or hash values in the image are limited due to digital signature and watermarking technology [26]. Active Image Forensics and Passive Image Forensics are the two methods of copy move forgery detection. Watermarking and digital signature are in the active methods, but the passive method does not based on pre-embedded data [27]. To catch the exact image without using of image forgery movement we employ forgery detection approach. Copy-move forgery

(2)

964

analysis is one of the most frequently used method. Mask a piece of image by shelter the other piece of image the copy move forgery is used [28].

Table 1: Forgery or manipulation attack types

Image Forgery Video Forgery VOIP Forgery Text forgery in images 1.)Copy move attack: Copy an

interested portion an pasting into the same image.

2.)Splicing or compositing:

Combining two images into one.

3.)Brightness, rotation, flipping, Gaussian noise, Gaussian blurring, Blurring, Additive white GN, JPEG compression etc. are attacks.

Example:

1.)Frame deletion attack: Cutting a frame or multiple frames from video.

2.)Frame insertion attack: Copying a frame from some other video and pasting.

Example: Adding ghosts frame in innocent video (Getting total number of watching count is one of the aim)

3.)Frame insertion and deletion attack:

inserting one frame and deleting some other frame.

4.)Morphing video making

5.)Political people’s funny video making

1.) Caller id spoofing 2.) Phone number spoofing 3.) Spoofing SMS 4.) Call from fake number.

5.) VOIP hijacking

1.)Data poisoning attack

2.)Cut paste attack 3.)Brightening attack 4.)Insertion of handwritten

signature, authority seal and format.

5.)Removal of watermark

6.)Metadata removal attack

7.) GPS tag removal attack

8.)EXIF-tag removal attack

9.)Metadata modifying and updating attack

Mainly attacks happening during client(s) and server communication. That is because the lack of security less communication channels. Attacks occurred because of freely available image editing tools.

Main concentration is on “EDIT” section of attack. Here research work carried out only based on

“copy-move attack”. Attacks are classified as direct and indirect.

In Direct attack section, the following are the attacks,

1. Add Image Processing Operations alone; 2. Add Image processing operation and filtering ; 3. Add more than one image processing operation alone ; 4. Add more than one image processing operations ; 5. Add more than one image processing operation and single filtering ; 6. Add more than one image processing operation and multiple filtering ;

In Indirect attack section, the following are the attacks, 1. Object removal attacks.

1.1. Signature adding or removal attacks ; 1.2. Metadata removal attacks ; 1.3. Watermark removal attack ; 1.4. Quality adjusting attack ; 1.5. Text adding or removal attack in image or misusing of others departmental formats.

(3)

965

Client(s) Server

3^rd Party Intruder

<org> <org>

Cloud Storage / Internet/Hospital

Healthcare Server Unit

<org>

<org>+EDIT

Where,

<Org+EDIT> is <Org+attacks> that is, <Forg>

or

<Forg>

Fig 1: Forgery attack

Windows MSPaint

Adobe PhotoShop

Server

<org>

Client(s)

3^rd Party Intruder

<org>

Multiple forgery(CLUE removal attack)

Client(s) <forg-forg>

GIMP

Fig 2: Transformations attack

Table 2: Image forgery attacks (Image(s) taken from CASIA dataset; attacks created in GIMP 2.10.4 for research purpose)

Copy Move attack Photomontage or Splicing or Image compositing

Resize Copying a part of an image

and pasting it into some other region of the image.

Combining 2 or more images and form one image.

Resizing the part of the image and recording.

(4)

966

Table 3: Forgery detection methods

Forgery detection Methods Techniques example(s)

Key point based SIFT,SURF etc.

Block based HOG,GLCM,Zernike Moments,LBP etc.

Table 4: Algorithm major steps

Important Steps Algorithm example(s)

Feature Extraction HOG,GLCM,LBP,SIFT,SURF,Zernike Moments,HU

moments, hybrid algorithms, local features, global features, statistical methods etc.

Feature Matching Machine learning, matching procedures.

Feature Selection BAT-algorithm:

FLBA,MOBA,KMBA,BBA,CBA,IBA,DLBA

Firefly, Cuckoo Search, PSO, ACO, Genetic algorithm etc.

Table 5: Dataset

Image database or dataset Images

CASIA – Image(s)

USC-SIPI – Image(s)

Kodak – Image(s)

(5)

967

MIASDbv1(J Suckling et.al.) – Medical image(s)

Flickr

CoMoFoD

COVERAGE

Dental x-rays

DDSM Contains format of LJPEG images. And the dataset mainly for breast cancer images like MIASDBv1.

Dresden

(6)

968

FAU

GRIP

MICC-F220

LITERATURE SURVEY CMFD:

Fan Yang et. al., proposed a CMFD based on hybrid features. Main methods used were KAZE and SIFT methods for this invention in feature extraction step. Transformations like rotation, scaling, JPEG compression and adding noise were used. In copy move forgery detection, performance measures mainly precision, recall and F1 measure were included for their research work. Before transformations of their forged images, it compared with some existing algorithms (SIFT, SURF, Zernike, Bravo) and new approach with based on transformations methods. And it located forged regions based on pixel level based on correlation coefficient map. Proposed method based on hybrid features achieved better forgery detection results than using single feature. In terms of precision, methods of Bravo-Solorio and Nandi (2011) and Ryu et al. (2010) obtained the best result in precision rate, reaching to 92.68%, 88.46%. In terms of the recall rate, the proposed method and the Zernike-based method are the best, reaching to 97.92% and 95.83%. Proposed algorithm contribution produced were P(90.27%), R(78.61%), F1(84.04%).

Yitian Wang et. al., proposed an efficient methodology for enhancing block matching based on Copy–

Move forgery detection. In that main feature is to get frequency of each block based on Fourier transform.

Main contribution focused and identified efficiency on applied image manipulations such as rotation, scaling, Gaussian blurring, brightness modification, JPEG compression and noise addition for their evaluation purpose. Main method used were Polar Fourier Representative Feature Extraction and lexicographic sorting and radix sort for feature matching stage. Proposed algorithm produced without modification (WM is 95.2%), under a rotation modification (RM is 62.5%), scale modification (SM is 65.5%), Gaussian blur (GB is 89.1%), noise addition (NA is 61.2%) and JPEG compression (JPEG is 46.3%).

Hesham Ahmed Alberry proposed a fast SIFT method for CMFD. Main concept used were Fuzzy c- means algorithm for clustering in order to reduce the detection time. Dataset used were MICC-220 dataset

(7)

969

and their own dataset for evaluation analysis. (TPR is 99.09%, FPR is 9.09%, Detection Time (hh:mm:ss) 00:13:52 and improvement is14.67 %) on images.

Mahmoud Emam et. al., proposed a Two-stage Key-point Detection Scheme for Region Duplication Forgery Detection in Digital Images. Main concept used in last stage post-processing, operations used were JPEG compression, noise addition, geometric transformations (scaling and rotation), and blurring. In feature matching stage were used k-NN, Euclidian distance and affine transformations methods. In case of plain copy move, proposed method produced P is 98.32, R is 59.76 and F1 is 74.34.

Mahmoud Emam et. al., proposed a PCET based copy-move forgery detection in images under geometric transforms. Methods used were Approximate Nearest Neighbor and morphological operations. Main aim were resulted to outcome robust to the geometric transformations with low computational complexity.

Analysis stage evaluated precision and recall and also evaluated plain copy-move, JPEG compression, Gaussian noise, Rotation and scaling transformations. In case of 512X512 image size, number of blocks were 231361 and feature vector dimensionality 8. And Precision P is 55%, Recall R is 100%, F1 is 70%.

Andrey Kuznetsov et. al., used image intensity range reduction, gradient calculation, expansion in orthonormal basis, adaptive linear contrast enhancement, LBP. In experimental section, concentrated to use Intensity shift (additive value -20, 20), Linear contrast (multiplicative .6, .9), Additive white noise (SNR (Signal to Noise Ratio) 10,300) for evaluation purpose and transformation detected based on SNR>100 criteria.

Jen-Chun Lee et. al., proposed a CMFD by HOG algorithm. Mainly used statistical features and lexicographic sorting method. And also used transformation operations like rotations, blurring, adjustment of brightness, and color reduction. Finally, used CoMoFoD dataset, compared with PCA, DCT, SIFT with designed algorithm. Proposed HOG, number of blocks produced 247,009 and feature dimension were 4.

Songpon TEERAKANOK et. al., authors used GLCM-based Rotation-invariant Feature for CMFD.

Designed procedure consists of methods SURF for keypoint selection, GLCM for Feature descriptor generation. Transformations used were brightness enhancement, rotation and correlation as preliminary approach. Used some threshold values and improved accuracy and also added existing classifier SVM.

Anil Dada Warbhe et. al., concluded the main drawback of block-based approaches. Designed algorithm detection procedure of copy-move forgery produced high computation time. In block-based approach, the image were divided into the number of blocks and each block processed for feature extraction and matching. In CMFD, large images taken high computation time if it is block based method, instead can use key-point based method to reduce the time. Correlation of the coarse blocks and fine-tuning is 0.8, 0.9, 0.94.

Amanjot Kaur LAMBA et. al., proposed a discrete fractional wavelet transform for CMFD. Main algorithm were used the case of reliable detection of CMF is discrete fractional wavelet transform. Core part of the algorithm used 14 equations in the proposed feature extraction algorithm. Usual steps in CMFD only used were lexicographic sorting, Euclidian distance and thresholding concept. Calculated precision and recall initially, further compared the results with some existing papers. And also used transformations mainly JPEG Compression and Gaussian Blurring concepts. Datasets used were CASIA V1 and 2 for their research work. Proposed outputted P is 99%, R is 100%. Accuracy of CASIA V1 is 99.65%, CASIA V2 is 99.01%. In post-processing, JPEG (QF is 90) then 76%, JPEG (QF is 70) then 62%, Gaussian Blur then 99%. Proposed method produced feature length 14, Block size 16 x 16, and Computation complexity 140 to 0 s depending on image size.

Hybrid CMFD:

Badal Soni et. al., proposed a Local Binary Pattern Histogram Fourier Feature for CMFD. The normal procedures were mentioned to detect manipulations in digital images and new equations has been formulated to calculate for their LBP-HF methodology. In feature matching, Euclidian distance and thresholding concept were used in the proposed algorithms. The algorithm was evaluated with CoMoFoD datasets which consist of 35 images and finally forged images were sorted. Proposed output produced Time 22.85%, TPR 98.4%, FPR 7.4%.

(8)

970

Yuan Wang et.al., proposed LBP-SVD Based CMFD. The general were used the combination of LBP algorithm and SVD algorithm. They used new approach for feature extraction, matching and marking stages. LBP-DCT compared with LBP-SVD with operation plain copy move and rotation 180degree. The drawback of their proposed system was, they were proved the effectiveness of the proposed method on the image scaling and other post processing regarding the characteristics of SVD in future. Proposed method produced higher accuracy after post processing stage also.

Cloud Computing & Security:

Mrs. Asma A. Shaikh discussed the cloud attacks mainly web security, browser security attack, cloud malware injection attack, flooding attack. Mainly added issues of cloud computing security, XML Signature Element Wrapping, Browser Security, Cloud Malware Injection Attack and Flooding Attacks, and its potential countermeasures. In flooding attack, one of the category was DoS, which, basically, is an action of sending a large number of false requests to a device. Paper given detail about cloud computing basics, characteristics, cloud computing delivery model. Main concept were XML signature attacks and its countermeasures in their study.

Kamile Nur Seviş et. al., discussed the detail about basics, servers examples, deployment services and platforms mainly in their article. Main contributions added were cons and pros of cloud computing and also basics of data integrity and its methods. Compared different research papers based on parameters such as ‘Integrity check method’, ’Static or dynamic operation’, advantages and disadvantages.

DIAO Zhe et. al., discussed about the architecture, characteristics, advantages of cloud computing.

Mainly added information regarding cloud storage and its structure. Reviewed about different layers and added more details about Dropbox, SkyDrive and Google drive and included about security risk analysis.

Fatma E.-Z. A. Elgamal et. al., authors mentioned two methods to secure transmission of medical image data between those parities while preserving the shared medical image from the distortion and to ensure the trust between authorized parties. Main algorithms used were spatial synchronization dynamic embedding, spatial synchronization dynamic extraction, 3D forward/backward cloud generators, Spatial synchronization dynamic and transform embedded and extraction algorithm, finally evaluated the analysis based on MSE, PSNR calculations.

BioMedical Field:

V. Díaz-Flores-García et. al., evaluated a dentist's ability to identify a manipulated dental X-ray images, when compared with the original images, used a variant of the methodology described by Visser and Kruger. Sixty-six dentists were invited to participate the manual analysis process. In that researchers included 20 intraoral dental X-ray images, 10 originals and 10 modified, manipulated by freely available tools like ‘Adobe Photoshop’ to simulate fillings, root canal treatments, etc. Mentioned that in that survey resulted very bad identification even if the doctors had more experience in the field of dentistry. Proposed method calculated binomial distribution of probability of correct answers, % of correct/incorrect answers by image set.

Filip L.G. Calberson et. al., mentioned detail view about digital radiography, image operations, image enhancement and its features. Main idea added were radiographic images manipulation and identification procedures. Methods used were to secure medical digital images. Main contribution were digital photographic verifier tools study and recent news regarding image manipulation happened in their field of research, digital radiography.

Ahmed Ghoneim et.al., proposed a new framework for medical imaging field and detected image manipulations. Used methods were noise map with multi resolution regression filter. One of the machine

(9)

971

learning approach called ELM, is nothing but Extreme Learning Machine classifier. These forgeries in medical image might lead to patient’s health and it might effect their life in threaten. In their analysis, used were different kernels and classifiers. Dataset used were CASIA V1 and 2 (Selected two natural images dataset and one mammogram). Accuracies were produced are 76.4%(SVM), 77.6(ELM), 84.3%(SVM+ELM).

Mohamed Elhoseny et. al., proposed a healthcare framework for medical images transmission and they mentioned in detail about various new algorithms for their work. Mainly added details about statistical parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Bit Error Rate (BER), Structural Similarity (SSIM), Structural Content (SC), and Correlation. Evaluation procedure compared newly defined approach with their base paper.

Post-processing:

Rahul Dixit et. al., discussed a review based article in that they mentioned classification and review of block based copy move forgery detection techniques. Three ways of classifications were used. Mainly classified as dimensionality reduction based techniques (PCA based, SVD based, PCA-DCT based), DCT based techniques (region duplication using DCT, Improved region duplication using DCT, Efficient region duplication using DCT), wavelet transform based techniques (Based on DyWT, DyWT with Zernike moments, CWT based). Mainly used three calculations for analysis work and also added plots and comparative tables.

Chun-Su Park et. al., proposed a fast and robust approach that can handle several geometric transformations including rotation, scaling, sheering, and reflection. Algorithm used were SIFT. Mainly authors used g2NN and affine transformation. Localizing forged region used by zero mean normalized cross-correlation (ZNCC). Methods used were SIFT-1 and 2 and followed by 2NN and g2NN. Used common noise sources, such as JPEG artifacts, Gaussian noise, additional scaling or rotation and also calculated precision, recall and F1-score measures for performance evaluation. Proposed method utilized to provide quantitative measures of image authenticity in criminal investigation, product inspection, journalism, intelligence services, and surveillance systems.

Toqeer Mahmood et.al., used Stationary Wavelet Transform (SWT) for their research work. Mainly used new feature extraction procedure, rest all same as existing copy move forgery detection step. Measures used were precision and recall. And also applied AWGN transformation. Proposed method produced in case of Blur (P(98.7-99.7%), R(2.7-5.9%)), in case of AWGN (P(95.9-99.5%), R(2.9-5.7%)).

Junliu Zhong et. al., proposed an improved block-based efficient method for CMFD. Mainly used the local and inner image feature, were extracted by the Discrete Radial Harmonic Fourier Moments (DRHFMs) with the overlapped circular block from the suspicious image. Generally 2Nearest Neighbors (2NN), Euclidian distance, correlation coefficient and morphological operations were used for plain CMFD. Applied transformations like geometrical distortions, Gaussian noise and JPEG compression on forged images for performance evaluation. In plain copy move, P is 94.7%, R is 91%. In proposed method under rotation post processing, (P is 90.1% and R is 90.8%), Gaussian Noise and JPEG compression of ( P and R is in 70-83%), Hybrid geometric distribution of ( P is 86.2% and R is 85.8%).

Classifiers:

Mohammad Farukh Hashmi et. al., proposed a method for image forgery detection for image authentication. Dataset used were CASIA dataset. Hidden Markov model and Support Vector Machine used for CMFD procedure. Kernel used were RBF kernel. Combined DCT statistics, LBP features with curvelet statistics and Gabor transform. Calculation focused were to find sensitivity, specificity, harmonic mean and accuracy. Overall accuracy were found to be 89%, Sensitivity and Specificity were found to be 90% and 88% respectively.

Guang-Bin Huang et. al., proposed a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs). It outputted good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark

(10)

972

function approximation and classification problems resulted that the new algorithm produced good generalization performance and faster than conventional popular learning algorithms for feedforward neural networks. Testing rate of diabetes were invented is 77.57%. Training case, time in minutes were 1.61 and testing case it were 0.71. Training rate, success in rate % is 92.35% in case of training, 90.21%

in case of testing, SV node is 200.

Guang-Bin Huang et. al., shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. It concluded that ELM provided a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly. Simulation resulted, ELM produced better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands of times) than traditional SVM and LS-SVM. In case of ELM, Gaussian kernel used RMSE and dev for testing, Signoid addictive node used RMSE and dev for testing and multi-quadrics RBF node were used in RMSE and dev for testing.

(I) Importance of Digital Forensics and Tools:

FRAUDULENT USE OF DIGITAL IMAGES AND DETECTION SURVEY:

Digital forensics investigation process is to examine and analyze the collected evidence. At present, cloud service(s) are used for storage and other application and system programs. Big data technologies are available to manage big data also. Machine and deep learning used to collect and analyze efficient and accurately huge collection of evidences.

Fig 3: Digital Forensics Tools

Digital forensics categorized into three generally. Image forgery, Audio / VoIP forgery, Video forgery and text/document forgery.

 Image Forgery: Copying an interested region and pasting into interested image.

 Audio / VoIP Forgery: Two case possible generally. They are the following, 1. Copying a region and inserting into genuine audio.

A. Copying a track from same file and inserting in the same audio or VoIP file.

B. Copying from different file and inserting in the same.

C. Changing metadata of the file.

D. Removing GPS tag of the file.

E. Injecting malware(s) to change a single bit of the file.

2. Watching Editing the digital image(s) by freely available tools like GIMP, Photoshop and other newly discovered tools.

 Video Forgery: Normally four general cases. Following are the cases, 1. Copying track from same file, pasting into same file.

2. Copying track from different file, pasting into same file.

3. Inserting the prepared track to falsify the evidence.

4. Removing the existing track to save the criminal from case.

 Text Forgery: Copying or removing interested portion from the image or document. Following are the possible cases,

1. Adjusting brightness or contrast of documents Digital Forensics

Image Audio / VoIP Video

Text/Do cument

(11)

973

2. Certificate forgery.

3. Fake documents preparations: Without the knowledge of police or government officials.

In digital image forensics, mainly there are four types of manipulations so far. That are the following,

1. Copy Move Forgery:- Copied an interested part “ ” from the image and pasted into interested region in the same image[26].

2. Splicing[28]: Combining 2 different images and form a new one. Example of online photo joiner is

“photojoiner.net”.

Other forgery possibilities are Image compositing[26], Photomontage[26] and Resize[26].

Manipulation in the name in the sense it`s a kind of forgery or attack for making normal things to abnormal and abnormal to normal to increase the crime rates.

Commonly used attack in the list mentioned above is "Copy move forgery". Many subdivisions can see in that type of attack category.

We were mentioned four types, but we listed out five in the above list, it`s because the 2, 3 method can categorize in one list. Splicing is to combine two different images as one and make one unique image.

Image compositing is a type of splicing, it can combine 2 or more images and produce a unique image.

And the expert professional’s manipulated work cannot identify by naked eye.

And image forgery attack(s) are available normally in the following fields, 1. Natural Image(s) Field

2. Medical field

2.1) Breast Cancer prediction

2.2) Dentist treatment Xray image Dataset 2.3) Other organ image set datasets

(12)

974

3. Satellite Image(s) Field

4. Agriculture Field, especially plant disease prediction

We worked out with the natural image(s) and Breast cancer mammograms.

Image manipulation detection :-

Detecting the manipulation in Image(s) there are some methods found. That are the following, 1. Key Point Based Method. Example: SIFT,SURF,....

2. Block Based Method. Example: HOG,ZERNIKE MOMENT,...

For Researchers in this field, the following are the algorithm of "Image Forgery Detection",

1. Data Acquisition: It's the initial step. Researcher's has to find out the interested data area to find out the suitable data-set(It's a database of image(s)) for their research work.

Example of data-set source(s) are the following,

CASIA, CoMoDoFD,USC-SIPI,Kodak, MIASDB V1 mammograms (J.Suckling et.al.)

Google Images( You may not find same XxY coords or HD print photographs, so may have to find out the extra time to remove noise(s) by predefined image noise filtering methods)

And other various image data-set(s).

2. Pre-processing: Remove the noise from the image you taken. And other process is to covert RGB image to Grayscale. In Image processing, initial step is to covert RGB to gray. Only in color Image processing, this converting not using. The RGB to Gray converting normally help to detect corner feature(s) very accurately than without converting process.

Average method is used for finding average of three colors Red, Green and Blue. But this is not the right method. Image can be properly converted to grayscale using only by weighted method.

RGB to Grayscale , (0.3 * R) + (0.59 * G) + (0.11 * B) [1]

3. Feature Extraction: Extract the feature(s) from an Image you have taken. Following are the example, Suppose we have a Phone. Feature(s) are it's height,weight, color,...

There are lots of feature extraction(s) algorithms available.

Researcher(s) can use either MATLAB or Python to extract features based on already existing algorithms or your own new contributions.

4. Feature Matching: Matching process is the current step. Example: Biometrics. It have 2 phases normally, enrollment: Taking example: fingerprint image(s) collecting in to the database for future verification process. Example: Biometrics. It have 2 phases normally, (a)enrollment: Taking example:

fingerprint image(s) collecting in to the database for future verification process. (b)recognition: Pressing finger on the machine to get authentication message. It's by verifying the current template and already loaded template. Template is the format of individual's fingerprint image(collected from individual's previously). Here feature(s) already collected for matching process.

Following are the 4 way(s) to do matching (classifying ), 1. Lexicographic sorting & Euclidian distance

2. Machine Learning and its algorithms usage.

3. Find out metadata by already existing websites or ethical hacking algorithms or own new approaches.

4. Researchers can do ‘post processing’ and do the analysis.

5. Forgery or manipulated Region Localisation: Locating the manipulated region.

6. Post-processing: Normally pixel level used to evaluate the accuracy of forged regions. There are lots of post processing methods. Example(s) are JPEG-Compression, Gaussian blurring, Gaussian Noise, Additive white Gaussian noise, Hue, Luminance, contrast ratio adjusting, likewise many methods available for post processing.

Digital forensics online tools:

photo-forensics

(13)

975

fotoforensics Ghiro Izitru JPEGSnoop

JPEG Compression analysis: You can see compression either single or double or no change.

HEX Viewer/editor: A small change in the image will be the manipulation.

Metadata EXIF Analysis(date,Time,Camera model,Location ,…)

Tineye – A Reverse Image search: Find out the original source of the image.

Smallseotools`s Reverse-image-search: Find out the source of any image you given.

Imageedited

Jeffrey's Exif Viewer Findexif

(II) Importance of Deep Learning and Tools:

IMPORTANCE OF DEEP LEARNING TOOLS SURVEY:

Deep learning consists of huge neural networks with lots of processing unit layers.

Python Deep Learning Libraries[30] are the following,

 DIGITS[30] used for training deep learning models in Caffe.

 The Microsoft Cognitive Toolkit[31] is a unified Deep Learning toolkit.

 PyTorch[31] is a Tensor and Dynamic neural network in Python.

 DeepLearning4J[31] is a deep learning programming library by Eclipse.

 Nilearn[32] is fast and easy statistical learning on NeuroImaging data.

 Orange3[32] is a data visualization for novice and expert.

 Fuel[32] provides your machine learning models with the data they need.

 Chainer[32] a standalone open source framework for deep learning models.

 Shogun[32] provides a wide range of unified and efficient Machine Learning (ML) methods.

 Neon[32] is Nervana’s Python-based deep learning library.

Deep Learning based tools are the following[33],

Applications are the following, Nvidia Deep

Learning AI

FloydHub DeepCrawl Deeplearnin

g4J Deep Art

Effects

MXNet Caffe Keras CNTK BigDL

LUSH DEEPMAT CXXNET Elektronn NeuralDesig

ner

SNAPCHAT TINDER Google maps Carrot APP

…...

(14)

976

Performance evaluation of Image forgery detection methods:

Results achieved for different feature extraction method and classifiers used.

Precision(P)= TP/(TP+FP) where TP,FP collect from confusion matrix based on algorithm and research method, and TP and FP are True and False positives.

Table 6 : Precision calculation based on different Image forgery detection methods Image forgery detection methods

Dataset(s) Number of

Image(s)

Zernike Moment GLCM SIFT

CoMoFoD 20 77.12 77.68 72.14

CASIA v1 20 76.289 78.124 73.257

CASIA v2 20 79.258 78.457 75.278

DDSM 20 72.48 74.28 74.12

MIASDBv1 20 75.175 76.584 72.345

In Table 6, tested five dataset(s) initially processed with 20 images to get best one among three algorithms. GLCM performed well in the two other algorithms. And used fusion of block based and key point algorithms, and in our analysis block based method is better than key-point based method. SIFT is a key-point based method and rest are block based method.

Lexicographic sorting and Euclidian distance method also evaluated, but that’s better for localization (single or multiple forgery) detection. In these reason, our next research will focus only based on machine learning algorithm and its fusion.

Table 7: Comparison of Processing Time (Pt) in seconds of SVM and ELM.

Algorithm Dataset(s) SVM ELM

GLCM NB-CASIA 1.75 .0325

MIASdbv1 1.7061 1.203

In table 7, tested natural and medical dataset based on GLCM algorithm(calculated as best one above) and we achieved low processing time in case of ELM, in these reason next research work will focus only ELM and the fusion of SVM and ELM.

Conclusion

Authenticity prediction of an image has turned out to be essential goal because of a freely available software(s) and skillful bad intention people. Experimental results have shown that this research gives high precision and low processing time results. In the experiments, the GLCM method outperforms some existing methods of image forgery detection. And the future work will focus about post processing with different operations to prove robustness and some hybrid methods to improve accuracy.

REFERENCES

1. Kamile Nur Seviş, Ensar Şeker, “Survey on Data Integrity in Cloud,” IEEE 3rd International Conference on Cyber Security and Cloud Computing, pp.167-171, 2016.

2. Asma A. Shaikh, “Attacks on Cloud Computing and its Counter measures,” International conference on Signal Processing, Communication, Power and Embedded System, pp.748-752, 2016.

3. DIAO Zhe, WANG Qinghong, SU Naizheng, ZHANG Yuhan, “Study on Data Security Policy Based On Cloud Storage,” International Conference on Big Data Security on Cloud, pp.145-149, 2017.

4. Fatma E.-Z. A. Elgamal, Noha A. Hikal, F.E.Z. Abou-Chadi,” Secure Medical Images Sharing over Cloud Computing environment,” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 5, pp.130-137, 2013.

5. Mohammad Farukh Hashmi and Avinash G. Keskar, “Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier”, International Journal of Security and Its Applications Vol. 9, No. 4 , pp. 125-140, 2015.

(15)

977

6. Guang-Bin Huang_, Qin-Yu Zhu, Chee-Kheong Siew, “Extreme learning machine: Theory and applications”, Science direct, Neurocomputing jounral , Vol.70, pp.489–501, doi:10.1016/j.neucom.2005.12.126, 2006.

7. Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, and Rui Zhang, “Extreme Learning Machine for Regression and Multiclass Classification”, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, Vol. 42, No. 2, April 2012.

8. V. Díaz-Flores-García, E. Labajo-Gonz_alez, A. Santiago-S_aez, B. Perea-P_erez, “Detecting the manipulation of digital clinical records in dental practice”, http://dx.doi.org/10.1016/j.radi.2017.05.003, Radiography, pp.1-5, 2017.

9. Filip L.G. Calberson, Geert M. Hommez and Roeland J. De Moor, “Fraudulent Use of Digital Radiography: Methods to Detect and Protect Digital Radiographs”, doi: 10.1016/j.joen.2008.01.019, 2008.

10. Ahmed Ghoneim, Ghulam Muhammad, Syed Umar Amin, and Brij Gupta, “Medical Image Forgery Detection for Smart Healthcare”, IEEE Communications Magazine, Advances in Next Generation Networking Technologies for Smart Healthcare, 10.1109/Mcom.2018.1700817, pp.32-37, 2018.

11. Mohamed Elhoseny*, Gustavo Ramírez-González, Osama M. Abu-Elnasr, Shihab A. Shawkat, Arunkumar N, Ahmed farouk,”Secure Medical Data Transmission Model for IoT-based Healthcare Systems,” IEEE, DOI 10.1109/ACCESS.2018.2817615,pp.1-15,2018

12. Toqeer Mahmood1, Tabassam Nawaz2, Zahid Mehmood2, Zakir Khan3, Mohsin Shah3, Rehan Ashraf, “Forensic Analysis of Copy-Move Forgery in Digital Images Using the Stationary Wavelets”, the sixth international conference on Innovative computing technology, pp.578-582, 2016.

13. Junliu Zhong, Yanfen Gan, Janson Young, Lian Huang, Peiyu Lin, “A new block-based method for copy move forgery detection under image geometric transforms”, DOI 10.1007/s11042-016-4201-9, Multimed Tools Appl, 2016.

14. Yuan Wang, Lihua Tian, Chen Li, “LBP-SVD Based Copy Move Forgery Detection Algorithm”, IEEE International Symposium on Multimedia, DOI 10.1109/ISM.2017.108, pp.553-556, 2017

15. Mahmoud Emam,Qi Han and Hongli Zhang, “Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital images”, journal of forensic sciences, doi: 10.1111/1556- 4029.13456, 2017.

16. Yitian Wang and Sei-ichiro Kamata, “Copy Move Image Forgery Detection Based on Polar Fourier Representation”, International Journal of Machine Learning and Computing, Vol. 8, No. 2, pp.158- 163, April 2018.

17. Hesham Ahmed Alberry, Abdelfatah Hegazy , Gouda i.Salama , “A Fast SIFT Based Method for Copy Move Forgery Detection”, Future Computing and Informatics Journal, 10.1016/j.fcij.2018.03.001, 2018.

18. Amanjot Kaur LAMBA, Neeru JINDAL, Sanjay SHARMA, “Digital image copy-move forgery detection based on discrete fractional wavelet transform”, Turkish Journal of Electrical Engineering &

Computer Sciences, vol.26, pp. 1261 – 1277, doi:10.3906/elk-1701-275, 2018.

19. Mahmoud Emam, Qi Han, Xiamu Niu, “PCET based copy-move forgery detection in images under geometric transforms”, Multimed Tools Appl, DOI 10.1007/s11042-015-2872-2, 2015.

(16)

978

20. Andrey Kuznetsova,b*, Vladislav Myasnikov, “A new copy-move forgery detection algorithm using image pre-processing procedure”, 3rd International Conference “Information Technology and Nanotechnology”, pp. 436–444, Samara, Russia, 2017.

21.Jen-Chun Lee a, Chien-Ping Chang b, Wei-Kuei Chen, “Detection of copy–move image forgery using histogram of orientated gradients”, Information Sciences, http://dx.doi.org/10.1016/j.ins.2015.03.009, 2015.

22. Badal Soni, Pradip K. Das, Dalton Meitei Thounaojam, “Copy-Move Tampering Detection based on Local Binary Pattern Histogram Fourier Feature”, https://doi.org/10.1145/3154979.3155001, ICCCT, Nov 24–26, Allahabad, India, 2017.

23. Songpon TEERAKANOK, Tetsutaro UEHARA, “Copy-move Forgery Detection using GLCM-based Rotation-invariant Feature: A Preliminary Research”, IEEE International Conference on Computer Software & Applications, pp. 365-369, DOI 10.1109/COMPSAC.2018.10259, 2018.

24. Anil Dada Warbhe, R. V. Dharaskar, V. M. Thakare, “A Scaling Robust Copy-Paste Tampering Detection for Digital Image Forensics”, 7th International Conference on Communication, Computing and Virtualization, Procedia Computer Science, Vol.79 , pp,458 – 465, 2016.

25. Fan Yanga, Jingwei Li, Wei Lu, Jian Weng, “Copy-move forgery detection based on hybrid features”, Engineering Applications of Artificial Intelligence, Vol.59, pp.73-83, 2017.

26. Arun Anoop M,” Image forgery and its detection: A survey”, International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS),13 August 2015,DOI:

10.1109/ICIIECS.2015.7193253

27. RGB-to-Grayscale-Conversions, Available: https://profs.info.uaic.ro/~ancai/DIP/lab/Lab_2_DIP.pdf 28. Muhammad Ali Qureshi, Mohamed Deriche,” A bibliography of pixel-based blind image forgery detection techniques”,Elsevier, Volume 39, Part A, November 2015, Pages 46-74,DOI:

10.1016/j.image.2015.08.008

29. Aditya. K, Slawomir Grzonkowski, NhienAn LEKHAC,” Enabling Trust in Deep Learning Models:

A Digital Forensics Case Study”, arXiv:1808.01196v1 [cs.CR] 3 Aug 2018.

30. Pythond deep learning libraries, Avilable: https://www.pyimagesearch.com/2016/06/27/my-top- 9-favorite-python-deep-learning-libraries/

31. Deep learning libraries and framework, Available: https://dzone.com/articles/11-deep-learning-with- python-libraries-and-framewo

32. Python based Projects, Available: https://www.dataquest.io/blog/top-20-python-ai-and-machine- learning-open-source-projects/

33. Machine learning software(s), Available: https://skymind.ai/wiki/machine-learning-library-software