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A detailed review of block based techniques in image copy move forgery detection ABSTRACT Digital images in every ones life depict very important role in various domains such as daily newspapers, magazines, multimedia, medical diagnosis, legal document and evidence in court.

In todays digital world image forensics is extremely demanding field as images are the key source of communication, fastest means to transfer and exchange of the information but now truthfulness of images are easily lost by using freely available editing software like photo shop, GIMP, Paint.NET which manipulate the content and deny the authenticity and integrity of images. So it has become a difficult task to determine an original image or a forged image. There are many types of image forgery but were focusing on Copy-Move forgery which is the most common type of image forgery, where one region of the image is copied and pasted to another region of the image such that it depicts as an original image.

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This paper provides a literature survey of different block based copy-move forgery detection techniques. Keywords image forgery image forgery detection copy-move forgery copy-move forgery detection image splicing image retouching copy move forgery methods block based methods 1. INTRODUCTION Digital Forensics is a branch of Computer Science deals with the investigation, verification and recovery of digital data and evidences found in digital devices such as computers, digital cameras or video recorders.

Digital images and videos act as the major sources of evidence towards any event or crime, 1 specifically in the legal industry as well as media and broadcast industries. Image forgery or image tampering is used in various areas such as surveillance systems, multimedia security, journalism, and scientific publications Such industries are the major application domains of digital image forensics. The present day wide availability of a large number of lowcost multimedia file processing tools, techniques and software, having numerous advanced features, has made editing and manipulation of multimedia data extremely easy44. All types of unauthorized modification or tampering of digital multimedia data pose as threats to their integrity. With the vast availability of digital image processing tools and techniques today, and the alarming number of digital images used in televisions, newspapers, posters, magazines and websites, the integrity of legal evidences used in the court of law, or the media and broadcast industries, 3is indeed at stake. Also when changed picture begins to spread on social networks that picture can make significant harm. It is therefore very important to be able to determine the originality of the picture. There are many algorithms or techniques for detecting tampered image.

In general, these techniques can be divided into two major groups Active Method and Passive Method, The following figure (1) shows the major classification of image forgery detection techniques. Figure 1 Classification of Image Forgery method A. Active approach In this active approach, the digital image requires some kind of pre-processing such as watermark embedded or signatures are generated at the time of creating the image. However, in practice this would limit their application. Digital watermarking 5 and signature are two main active protection techniques, as something are embedded into images when they are obtained. We can detect the Image is tampered, if special information cannot be extracted from that obtained image Regions of the image.

In recent times, various schemes are proposed for providing security to the image, which is analogous to the concept of watermarking like, message authentication code, image hash, image checksum and image shielding as a counterpart to it. Watermarking is such a method of active tampering detection, as a security structure is embedded into the image, but most present imaging devices do not contain any watermarking or signature module and that are similar to the application of active protection. This structure is used for integrity evaluation in the sense that if any discrepancy is found with the structure then the image is tampered and an inverse analysis over the structure is done to locate tampered The main drawback of these approaches remains that they are to be inserted into the images at the time of video recording using special equipments thus prior information about image becomes essential. B.

Passive approach It is the process of authenticating images with no requirement of prior information just the image itself. Passive techniques are based on the assumption that even though tampering may not leave any visual trace but they are likely to alter the underlying statistics. Passive techniques are further classified as forgery dependent methods and forgery independent methods. Forgery dependent detection methods are designed to detect only certain type of forgeries such as copy-move and splicing which are dependent on the type of forgery carried out on the image while as forgery independent methods detect forgeries independent of forgery type but based on artifact traces left during process of re-sampling due to lighting inconsistencies.

The main objective of passive detection technique remains to classify a given image as original or tampered. Most of the existing techniques extract features from image after that select a suitable classifier and then classify the features. The rest of this paper is organised as follows. In Section 2, we analysed the types of Passive blind approaches, Section 3 described Classification of Copy Move Forgery Detection (CMFD) techniques, Section 4 explained the General workflow of CMFD techniques and.

Section 5 demonstrate Methods of Block based CMFD techniques and survey. 2. Types of BLIND (PASSIVE) Image Forgery A. Image Retouching Image Retouching is less harmful kind of digital image forgery than other types present.

In case of image retouching original image does not significantly changes, but there is enhancement or reduces certain feature of original image.39 This technique is popular among photo editors they employ this technique to enhance certain features of an image so that it is more attractive. Actually, the fact is that such enhancement is ethically wrong. Figure.

(2.a) shows the example of Image Retouching Figure.(2.a) Example of Image Retouching B. Image splicing or photomontage Image splicing is a technique in which copies and pastes regions of the image from the same or different image. This is a fundamental step used in digital photomontage, which is very popular in digital image content editing.

It is also referred as paste up produced by combining the images together using available digital software tools such as Photoshop. The spliced image used in many ways such as news reports, photography contest, key proof in the academic papers, and so on, which could bring certain negative influences. There is composition of two or more images, which are combined to create a fake image. Figure (2.

b) describes how two images are spliced to form a third one by copying a spliced portion from the source image into a target image, it is a composite picture of scenery which is forge image. . Figure (2.b) shows the example of Image splicing C. Copy-Move Approach The copy move forgery is the difficult and most commonly used kind of image tampering technique. In this technique, one needs to cover a part of the image in order to add or remove information. Copying from one part and pasting the same in some other part in the same image with an intention to hide certain content in the original image or duplicating some content that is not actually present in the image.

. . Since the copy-paste is within an image, properties of the tampered portion will be same as that of other regions and it is difficult for human eye to detect forgery. The example of Copy-Move type in Figure .

(2.c) shows the example of Copy-move is as shown below. Original image is on the Left and the forged image on the Right 3. Classification of Copy Move Forgery Detection Techniques Block and key-points based techniques are mostly used in copy-move forgery detection.17 The following Figure (3) shows the two types of Copy Move Forgery Detection techniques (CMFD). Figure (3) Types of copy-move forgery detection. Block-based techniques Key points-based techniques Here the key point detector algorithms46 are used to identify the key points. Then the feature matching is performed by comparing the feature vectors which is extracted from a region around these key points.

It extracts feature point using different methods like SIFT, SURF etc without any image subdivision. The approaches like clustering, Euclidean distance, the nearest neighbour etc can be used for feature point matching . 4. GENERAL STEPS IN COPY-MOVE FORGERY DETECTION In copy-move forgery, there exists a strong relationship between the copied and pasted parts which can be used as evidence for detecting copy-move forgery. The typical step for copy-move forgery detection is depicted in Fig.

4. (a) Image pre-processing The aim of pre-processing is the improvement of image data that suppresses unwanted distortions or enhances some image features important for further detection. The aim of pre-processing is the improvement of image data that suppresses unwanted distortions or enhances image features important for further detection. The given image is converted into grey-scale (color conversion)when applicable (except for algorithms that require color channels). I0.299R0.587G0.114B where R,G,B are three input channels, and I is obtained the gray image Other pre-processing techniques includes, dimension reduction, image resizing, low-pass filtering etc.

The given image is converted into grey-scale (color conversion)when applicable (except for algorithms that require color channels). I0.299R0.587G0.114B where R,G,B are three input channels, and I is obtained the gray image Other pre-processing techniques includes, dimension reduction, image resizing, low-pass filtering etc. (b) Feature extraction The goal of feature extraction is to compute the specific representation of the data that can highlight relevant information. The dimension of the feature space constructed by the feature extraction techniques may be prohibitive, can involve redundant information and can cause the efficiency of a classifier to deteriorate.

The feature selection process is then used to reduce system complexity and time complexity by eliminating the insignificant features before classification.. (c) Feature matching After feature extraction, the potential copy-move pairs are identified by searching blocks with similar features. High similarity between feature descriptors can be interpreted as duplicated regions.

In block-based method lexicographically sort similar features and Best-Bin-First search method to get approximate nearest neighbour in key-point based methods helps in the feature matching. (d) Filtering A single similarity criterion is not enough to claim the presence/absence of duplicated regions. Filtering schemes are thus used to reduce probability of false matches. (e) Post-processing.

The goal of this step is to preserve and highlight the matched regions of the image that exhibited common properties. In this paper, we are going to discuss various block based CMFD methods used in image forgery. 5.. Literature Survey for Block-based CMFD techniques The block-based approach starts by partitioning the tampered image into overlapping or non-overlapping blocks. This division is n followed by robust features extraction from each block and features matching in block pairs. In the matching step, the block features are sorted or arranged using appropriate data structures and forgery decision is based on the similarity of the adjacent block features pairs.

In this study, block-based approaches are divided into five different categories based on the block feature extraction technique. 5.1. Frequency domain-based methods Varsha Sharma, Swati Jha 39 proposed Discrete Cosine Transform (DCT) is used to represent the features of overlapping blocks Here the give gray scale image is converted to overlapping blocks and then apply DCT to represent block features and block features are represented as row vectors and sorted Matching was done and blocks are discarded those do not belong to potential cluster. Then pixels are coloured in duplicated region to identify duplication This method has addressed the issue successfully and is considerably faster than the other method. It has detected forgery with good success rate. Also, it has shown robustness against added Gaussian noise, JPEG compression and small amount of scaling and rotation. Hajar Moradi-Gharghani 41 In this method, feature vectors are extracted using DCT transform from non-overlapping blocks of the image.

Then, these vectors are lexicographically sorted. Copied blocks are selected from identical vectors based on some criteria.Simulation results show that, the method can effectively detect forgery in the image with very small False-Positive in comparison with the classic methods in terms of FPR(False positive rate)i and DAR Detection accuracy rate) . The method can effectively find regular, irregular and multiple forged sections Amanjot Kaur LAMBA 18 focussed discrete fractional wavelet transform-based scheme for identification of duplicated regions in the image. The test image is split into overlapping image blocks with dimensions.

Then, on each image block, discrete fractional wavelet transform is used for the features extraction. All the feature vectors are systematized in lexicographical manner followed by the block matching and block altering steps to obtain the replicated blocks, if any. These methods detect single and multiple duplicated regions successfully. This forgery detection scheme can detect tampering areas even in the presence of distortions due to Gaussian blurring and JPEG compression.

Ms. Aarti S. Deshpande, Ms. Ashwini S. Shinde 19 proposed Fast Fourier Transform (FFT) coefficients have been evaluated for non-overlapping blocks at each pixel neighbourhood for similarity matching. Mean has been calculated for each block and each block is put into a particular box of four pixel intensities such that 256 intensity values has been divided into 64 categories . After FFT coefficients has been used for similar matching in which the Euclidean distance and the actual coordinate distance of the two blocks has been used so that different matching locations can be detected. Based on these location, whole image is divided into two categories i.

e. Forged and non-forged area. For better accuracy morphological operations has been implemented on the segmented forged pixels so that noisy or mis-detected forgery areas can be eliminated. Proposed method gives 999-100 accuracy in copy move forgery detection. Change of intensity in the copy moved part is successfully addressed in this algorithm. Rahul Dixit 16 discussed a regionduplication detection technique which utilizes the decimated Dyadic Wavelet Transform for its operation.

The authors divided an image into pixel submatrices or blocks and aim to find matches among different image blocks, so as to detect image region duplications. Matching between block features which can be extracted using the DyWT method, is computed using the Canberra distance formula. This method attains considerably high detection accuracy. Fattah, S.

A., Ullah 42, proposed two-dimensional DWT is applied on the forged image and block division is performed on approximate DWT coefficients. Matching is performed by calculating the distance between all block pairs. This method is tested on images of MICC dataset. Since DWT is variant to rotation and scaling.

. These methods are invariant to many post-processing operations such as compression, blurring, and noise. However, these methods are less capable of handling geometric transformation attacks because frequency based block features are variants to a geometric transformation. In summary, in this sub-section, we have discussed various frequency domain methods.

These methods are invariant to many post-processing operations such as compression, blurring, and noise. However, these methods are less capable of handling geometric transformation attacks because frequency based block features are variants to a geometric transformation 5.2. Dimensionality reduction based methods Ms. Aarti S. Deshpande, Ms. Ashwini S.

Shinde 19 used an improved algorithm based on Singular Value Decomposition (SVD) to detect this image forgery. In this method after applying image pre-processing operations the image is divided in number of overlapping blocks. The SV features are extracted from each block. All these SV features are then lexicographically sorted so the blocks with similar feature come near to each other. By using Euclidean distance Shift vector concept we can locate the copy move region in the image. Copy-Move type of forgery can be easily and effectively detected by SVD.

SVD algorithm requires lower time than PCA in detection method. It is more robust to post image processing operations and give proper results for naturally duplicated region. It has high value of matching ratio. As overlapping block size increases the total time required for detection decreases but false negative rate (F.N.

R) increases. Kaushik, Rajeev, Bajaj 26 presented a hybrid approach which involves 2D-DWT and SVD. It is fast, efficient and accurate for tampering detection. In this approach, a color image is provided for the possible detection of forgery. Then, B,G and R channels are extracted and 2D-DWT is applied to each channel and approximated coefficient are extracted for further processing.

These approximated coefficients are separated by sliding a window of size bx b which separate them into coinciding blocks. The dimension of each block is reduced by applying SVD. These R, G and B channel blocks are stored in a row matrix. From the row, matrix separates the non-overlapping blocks and compute the Euclidian distance between them and if it is more than the threshold Ct than it is considered as a candidate block. The distance between these candidate blocks and the coinciding blocks is calculated, a threshold Cd is selected if the computed distance is below than Cd then it is considered to be forged Varsha Karbhari Sanap11 presented new hybrid method Region Duplication Forgery Detection in Digital Images Based on 2D-Discrete Wavelet Transform(2D-DWT) and Singular Value Decomposition(SVD) with goal of improving the performances in terms of hit rate, miss rate and false detection rate. Extending this method by using SVD with it so that accurate forgery area can be localized.

In this method, each layer of RGB image is extracted and then 2D-DWTis performed on forged image. It then divides LL sub-band into overlapping blocks. SVD on each block are performed and sorted it into bucket groups to get dominant feature. Performance of forgery detection is measured in terms of hit ratio, miss ratio and FDR.

Michael Zimba, Sun Xingming 40,developed an improved algorithm based on Discrete Wavelet Transform (DWT) and Principal Component Analysis-Eigen value Decomposition (PCA-EVD) to detect copy-move digital image forgery is proposed, and experimental results indicate that the dimension of the features is reduced compared with the existing related algorithms, at the same time, the accuracy of detection is good. This scheme accurately detects such specific image manipulations as long as the copied region is not rotated or scaled. This method reduces the features matching time thereby increasing the overall processing speed. From the literature study, it has been found that many dimension reduction techniques have been used in CMFD to reduce the dimension of extracted block features, it reduces features matching time thereby increasing the overall processing speed. 5.3. Local binary pattern (LBP)-based methods BesteUstubioglu,GuzinUlutasMustafa Ulutast, 27suggested the method which divides the image into overlapping blocks.

Labeled blocks LBP (Local Binary Pattern)are transformed into frequency domain using DCT (Discrete Cosine Transform) and feature vectors are developed for the block. Then the feature vectors are lexicographically sorted and is used to determine the forged blocks. This method has higher accuracy ratios and lower false negative values under some post processing operation compared to other DCT based methods.

This method can also detect multiple copy move forgery. HYPERLINK https//www.researchgate.net/profile/Salam_AlnesarawiSalam Abdul-Nabi Alnesarawi,28 presented for detection of copy move forgery using completed robust local binary pattern (CRLBP). Here the suspicious image is filtered using a hybrid filter before being divided into overlapping blocks. An invariant rotation descriptor is produced from the CRLBP operator to extract feature for each block and these feature are sorted by using lexicographical sorting. Euclidean distances are used to identify the forged regions by comparing with the feature vector The authors introduced a new technique to reduce false matches which caused by flat region. This approach is able to detect forged areas efficiently even in the presence of image distortion such as rotation, additive noise, blurring and compression.

And can also solve the false match problem. It give results with precision and a false positive rate give better performance Vandana Sangwan, Madan Lal 20proposed a method to detect copy-move image forgery using CS-LBP (centre symmetric local binary pattern),is used for feature extraction . In this method, Gray level conversion is performed on the given image. Then the image will be decomposed into fixed sized non-overlapping blocks.. The distances between extracted feature vectors is calculated and are sorted lexicographically, and retained only those pairs of blocks that have minimal distances between them. Those minimal distances compared with predefined threshold value serve as the purpose of the copied regions. Then post-processing is done using morphological operation and mask is generated for the region detected as forged region.

HYPERLINK https//www.ncbi.nlm.nih.gov/pubmed/termDavarzani20R5BAuthor5Dcauthortruecauthor_uid23890617Davarzani R1, HYPERLINK https//www.

ncbi.nlm.nih.gov/pubmed/termYaghmaie20K5BAuthor5Dcauthortruecauthor_uid23890617 Yaghmaie K, HYPERLINK https//www.ncbi.nlm.nih.gov/pubmed/termMozaffari20S5BAuthor5Dcauthortruecauthor_uid23890617 Mozaffari S, HYPERLINK https//www.

ncbi.nlm.nih.gov/pubmed/termTapak20M5BAuthor5Dcauthortruecauthor_uid23890617 Tapak M, 29 presented an efficient method for copy-move forgery detection using Multi resolution Local Binary Patterns (MLBP). This method is robust to geometric distortions and illumination variations of duplicated regions and also recovers parameters of the geometric transformations. The given image is divided into overlapping blocks and feature vectors for each block are extracted using LBP operators.

The feature vectors are sorted based on lexicographical order. Duplicated image blocks are determined in the block matching step using k-d tree for more time reduction. Finally, in order to both determine the parameters of geometric transformations and remove the possible false matches, RANSAC (RANdom SAmple Consensus) algorithm is used. This method detect duplicated regions even after distortions such as rotation, scaling, JPEG compression, blurring and noise adding.

Dijana Tralic1 Sonja Grgic1 43 analyzed method that combines Cellular Automata (CA) and Local Binary Patterns (LBP) to extract feature vectors for the purpose of detection of duplicated regions. Both CA and LBP develop a simple and reduced description of texture in the form of CA rules. CA is a very simple set of rules can be used to describe complex textures that represents local changes in pixel luminance values, while LBP, applied locally, allows efficient binary representation.

CA rules are formed on a circular neighbourhood, resulting in insensitivity to rotation of duplicated regions. This method showed good performance in the case of plain/multiple copy-move forgeries and rotation/scaling of duplicated regions, as well as robustness to post-processing methods such as blurring, addition of noise and JPEG compression. An important advantage of this is low computational complexity and simplicity of its feature vector representation. 5.4. Texture-based methods Ghulam Muhammad et al.

7 presented the method to detect forgery in which Gabor filter is used as feature extraction method with different orientations and scales are applied to an image then DCT is calculated from all the filter outputs and DCT coefficients are concatenated to form feature vector. Raichel Philip Yohannan 14proposed an algorithm in which s feature vectors extracted from the Gabor response of each overlapping block of the image. Input images are divided into sub-blocks, For every image sub block, feature vectors are formed using rotation-invariant Gabor representations. These feature vectors are robust representations of the corresponding sub-blocks.

Every time a feature vector is computed, it goes into the feature matrix A. The feature matrix represents the whole image, and it is used for further processing. Then the vectors are sorted in a lexicographic order , find the matching vectors and grouped the similar ones The matching blocks are then colored with the same color in a map image and thus identified as segments that might have been copied and moved. Musaed alhuseed,31proposed a new image tampering detection method based on local texture descriptor and extreme learning machine (ELM). The forged image may be both splicing and copy-move forgery. First, the image was decomposed into three color channels (one luminance and two Chroma), and each channel was divided into non-overlapping blocks. Local textures in the form of local binary pattern (LBP) were extracted from each block. The histograms of the patterns of all the blocks were concatenated to form a feature vector.

The feature vector was then fed to an ELM for classification. Xiuli Bi, Chi Man Pun, Xiao Chen Yuan, 32 an Adaptive Polar based Filtering Method is proposed for image copy-move forgery detection. To filter out the redundant pixels from the initially matched pixels, two pixels sets-Symmetrical Matched Pixels set and Unsymmetrical Matched Pixels set, are extracted from the matched pixel pairs, furthermore, the polar distributions of the two sets are calculated respectively. Then, the filtering thresholds can be adaptively calculated according to the polar distribution, thus the redundant pixels can be filtered out accordingly. Finally, some morphological operations are applied to the remained pixels to generate the detected forged regions. This scheme can achieve much better detection results . In summary, texture-based methods are robust against contrast modification, Gaussian blurring and JPEG compression.

However, these methods are not robust against geometric transformation threats. 5.5.

Moment invariant-based methods Xiang-Yang, W., Yu-Nan, L., Huan, X., et al.

33proposed a new multi-granularity super pixels matching based algorithm for accurate detection and localization of copy-move forgeries, which integrated the advantages of key point-based and block-based forgery detection approaches. Steps(i) The original tempted image is divided into non-overlapping and irregular coarse-granularity super pixels, and the stable image key points are extracted from each coarse-granularity super pixel. (ii), The super pixel features, which is quaternion exponent moments magnitudes, are extracted from each coarse-granularity super pixel, and the matching coarse-granularity super pixels (suspected forgery region pairs) rapidly using the Exact Euclidean Locality Sensitive Hashing (E2LSH). (iii) The suspected forgery region pairs are further segmented into fine-granularity super pixels, and the matching key points within the suspected forgery region pairs are replaced with the fine-granularity super pixels. Finally, the neighbouring fine-granularity super pixels are merged, and obtained the detected forgery regions through morphological operation.. Yu Sun, Rongrong Ni 44 Here authors divided the image into two different types of regions.

Features are extracted using SIFT for texture region and and smooth region by using Zernike moments, Non overlapping blocks are used as candidates in smooth area and the registration was done via the phase correlation algorithm before matching. Then, the exact forgery regions will be generated after removing falsely matched pairs and exploiting morphology operations. Tao Wang, Jin Tang 13proposed a new region duplicated detection method to automatically detect and localize duplicated regions in digital images. This method is based on merging blur and affine moment invariants, which allows successful detection of region duplication forgery, even under some simple affine transforms and blur degradations.. These demonstrate that this method is an effective way to detect the duplication regions under some simple affine transforms and blur degradations blindly.

S. Ryu, M. Kirchner, M. Lee, and H. Lee,34 focused on the localization and detection of copy rotate move (CRM) manipulations, for which Zernike moments are applied as robust feature representation of small overlapping image blocks.

Zernike moments are recognized for their analytical invariance to rotation. Here detector employs a novel block matching paradigm based on locality sensitive hashing, and it exploits phase differences of Zernike moments in a feature-based error reduction approach. As to the limitations, detectors based on Zernike moments are noted inherently incapable of localizing duplicated regions that underwent strong affine transformations other than rotation. B. Ustubioglu, G, Ulutas, M. Ulutas 35analyzed the image based on the colour moments and colour layout descriptors (CLD) .

The method divides the image into overlapping blocks. First colour moments of the blocks are used to group similar blocks into clusters. Then the similar blocks are mapped into the same cluster. Thus, search for copied and moved regions in clusters to detect forgery can be done by separate threads with CLD descriptors that are used to extract block features. The above procedure makes this method is more robust to post-processing operations. This approach can also detect copy move forgery even if additive white Gaussian noise, JPEG compression or Gaussian blurring is used after forgery. In summary, it can be noted that moment-based methods are invariant to different post-processing operations as well as geometric transformation attacks.

However, these methods are computationally more expensive than others 6. CONCLUSION Detection of forgery in images is essential because in many places images are used as a primary component for communication. Efficient and viable algorithms are necessary to detect the forged region. In this paper, we have discussed different types of image forgery and we have given an overview on the Block Based Techniques for detection of copy move forgery. An attempt has been made to clearly present each step of the different existing techniques.

From the literature survey, we observed that the big problem with the copy move forgery in digital image is the detection of duplicated region processed by some common post processing operations such as compression, noise addition, rotation, scaling, flipping etc. The other concern is the time complexity of detection technique of copy move forgery in digital image. The techniques which are discussed here has some drawbacks and consequently, there is still more work to be done to perfect them. There must be development of new techniques and algorithms which can solve the tampering problems thereby solving problems in many areas like criminal cases and can be accepted as official documents for implementation. The scope in image forensics is vast and needs new tools and detection methods to propose and implement new approaches. 7.

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on Circuits and Systems (MWSCAS), 2014, pp. 801804 Dijana Tralic1 Sonja Grgic1 Xianfang Sun2 Paul L. Rosin2, Combining cellular automata and local binary patterns for copy-move forgery detection, Springer ScienceBusiness Media New York 2015, Multimed Tools Appl Yitian Wang and Sei-ichiro Kamata, Copy Move Image Forgery Detection Basedon Polar Fourier Representation, International Journal of Machine Learning and Computing, Vol. 8, No. 2, April 2018 gJtaA1LXJ10zmsPz8UGYQxkV.pIgU5tK VhPs (dwOoFKWk-OHnGZ csvHSp. ,ni5BmBVl)Vwht/ZDQYT )ah7t iBN(kvZof_ SpicvaC/aoCxRlfw-0lW t5uxeZr 61aQh [email protected] [email protected]_S)Iicb00ZJJ aibc18AbLZ3kNJe)74YUayhTE_LE_zv3RSkjcTo5 C8Xf BTuZWH [email protected] nUbvitVm-Si7ifgRFHf dqm.

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