Image forgery detection

ABSTRACT: Image forgery detection system is used to identify the authenticity of an image. With the availability of digital software and high resolution hardware, digital images can be easily tampered and manipulated. This is because now a days digital images can be manipulated in such a way that forgery cannot be detected visibly. This vogue directs major compulsion and decline the reliability of digital images. This paper presents comparative results of Wavelet Coefficients for copy move image forgery detection.

extracted feature vectors are then sorted using the radix sort. DWT and SIFT [2] algorithms are proposed for copy-move detection. With DWT, the low frequency information or image is obtained. With SIFT robustness is introduced, here it detect forgery of the image even it is copied, rotate scale and then pasted. In [3], survey done on various image forgery detection techniques and finally conclude the comparative study with some parameters. Also tools are mentioned to detect the forged images that travel over the network or by natural way for daily forensics, image processing, and security. Salam A.Thajeel [4], discussed digital image forensics and its types, challenges and research problems and detail analysis of the existing approaches for detect image tampering. Author also discussed block based method and key point-based method and popular techniques of two methods. Moreover, most of the methods may not address the problems. Therefore, there is a need to develop techniques that is efficient to deal with these challenges. The Speeded Up Robust Features (SURF) [6] were applied to extract features instead of SIFT. However, although these methods can locate the matched key-points, most of them cannot locate the forgery regions very well; therefore, they cannot achieve satisfactory

The process of altering an image for the sake of deceiving or change the public perception is called as ―Forgery‖. Image forgery detection plays a significant role as digital images play a significant role in simplifying the way of representing and transferring ideas flexibly. There are two techniques for detecting the forgery. They are Active approach and Passive approach. In Active approach, it requires prior knowledge of the original image and it also requires human intervention and some specially equipped cameras. Because of these drawbacks, we chose passive approach. Passive approach is again divided into three types. They are Image Splicing, Image re-sampling and Copy-move forgery. Copy- move forgery is most commonly used forgery technique.

Among other evidences, illumination inconsistencies are potentially effective for splicing detection. Illumination inconsistencies occur in a forged image because, for a manipulator proper adjustment of the illumination conditions is difficult to achieve while performing forgery The previous methods based on illumination inconsistencies concentrates on region based approach i.e. their method can be applied to image with faces only. Also they use a single algorithm for illuminant color estimation. So their accuracy is low. So in this work in order to improve accuracy, illumination inconsistencies are combined to produce a new technique for image forgery detection.

over the past few years, a trend which opens the door to perform image forgery. Image forgery has become a critical concern in many applications. Common techniques used to create forged digital images that are Copy-move and image splicing. The existing system integrates block-based and key point-based forgery detection methods with SLIC Super-pixel Segmentation algorithm. Adaptively, this algorithm segments the host image into non-overlapping and irregular blocks, the feature points are extracted from each block, and the block features are matched with one another to locate the labelled feature points. Existing procedure can approximately indicate the suspected forgery regions. SLIC image segmentation used single and global stopping criterion which reduced the accuracy of image forgery detection and it detects copy-move image forgery. The main contribution of this proposed work is to use a local termination criterion for each cluster to avoid revisiting clusters and image areas without any major changes since the last iteration. Pre-emptively stops the evolution of segment boundaries in homogeneous image regions. Another contribution of this work is to detect splicing attack also.

Nevertheless, MIFT feature extractor introduced by Kalyani Khuspe et. al., in [17] with a novel methodology for identification of Copy-Move digital image forgery by applying keypoint-based MIFT features that did not only referred the attributes of SIFT feature extractor, but also it was robust against the mirror reflection transformations. Proposed method also performed clustering on the set of images afterwards extraction of the MIFT features executed and codebook has been generated by the centroid of each cluster and codebook employed for intercommunication between recipient and transmitter. Although, SURF is next version of SIFT feature extractor as well as SURF is fast over SIFT. Key-based feature extractor SURF neglected by researchers and very few researchers employed SURF to deal with image forgery detection and Shinfeng D. Lin et. al., in paper [4] practiced SURF for extraction of features that were helpful in forged region detection as well as it shown robustness against rotation and scaling.

Watermarking is also used for image forgery detection. In Checksum schema that it can add data into last most significant bit of pixels. A maximal length linear shift register sequence to the pixel data and so determine the watermark by computing the spatial cross-correlation function of the sequence and also the watermarked image. These watermarks are designed to be undetectable, or to blend in with natural camera or scanner noise. Visible watermarks also exist. In addition to this, a visually undetectable watermarking schema is also available which can sense the modification in single pixels and it can find wherever the modification occur [2]. Active techniques have some disadvantages because they required some human involvement or specially equipped cameras. To overcome this drawback a passive authentication has been proposed.

The goal of blind image forensics is to determine the authenticity of an im- age without using an embedded security scheme. With the broad availability of digital images and tools for image editing, it becomes increasingly important to detect malicious manipulations. Consequently, image forensics has recently gained considerable attention. Most existing methods fall into two categories: a) detecting traces of a particular manipulation operation and b) verifying the “rationality” of expected image artifacts. Good surveys on such methods are [10, 4]. For instance, methods for copy-move forgery detection search for duplicated content within the same image (see e. g. [3]). However, traces from such a copy- ing operation may be visible to the eye. If a manipulator is careful, he might hide such visible traces using post-processing operations. To counter a careful, yet not technically educated forger, a number of researchers focused on invisible cues for image manipulation. One of the most widely used invisible indicators are JPEG artifacts. Their use in image forgery detection is based on the following key observation: every time that a JPEG image is recompressed, the statistics of its compression coefficients slightly change.

Digital image plays an important role in our daily life. Today most of the scientific journals, medical record , newspaper, magazine etc contains the digital images. Facebook alone has over three billion photos uploaded to its website every month. The growing use of digital images has also prompted the development of numerous image-processing software programs, many of which are free to general public. Due to availability of large number of free photo editing software it is very easy & simple to manipulate the digital images without leaving any traces of tampering. As a result of this, digital evidence have not yet been accepted as a proof in crimi- nal investigation. The dictionary meaning of forgery is part of images is copied and pasted to conceal a person or object in the scene or sometimes to clone an object. Image forgery detection is probably one of the most interesting functions under digital image for- gery due to its application which is generally much closer to the public. It deals with techniques or algorithm to detect traces of digi- tal image tampering. The availability of any of these traces is proof that an image has been tampered. There are many algorithms or techniques for detecting tampered image. In general, these techniques can be

Image forgery detection plays an important role in image forensics, most of the existing methods aimed at focusing coarse grained forgery localization. In this paper, we introduce tamper detection techniques based on artifacts created by Color Filter Array (CFA) processing in most digital cameras. We make the assumption that tampering removes the artifact due to de-mosaicking algorithm. We focus our attention on the fine grained forgery localization problem, assuming to have no information on the position of possibly manipulated pixel. The proposed method is based on a new feature measuring the presence of de-mosaicking artifact at local level. We proposed a new feature measuring the presence of de-mosaicking artifact even at the smallest 2 X 2 block level with the help of this method we are able to find fine grained forgery localization .

With the rapid development of multimedia technology and availability of powerful digital media editing tools such as Photoshop, PIXLR etc., it is possible to manipulate (or forge) digital images very easily. For humans, it becomes very difficult to identify visually whether the image is original or manipulated. The detection of image forgery is very important because an image carry’s a lot of important information and can be used as legal evidence in medical imaging, image forensics, news media, and the court of law and in many other fields. There are many techniques to manipulate the digital images such as copy-move, image splicing, resampling and soon. Among them, the common form of image forgery is copy move forgery. In this paper reviews of the various copy-move digital image forgery detection techniques are presented.

detection. With DWT, the low frequency information or image is obtained. With SIFT robustness is introduced, here it detect forgery of the image even it is copied, rotate scale and then pasted. In [3], survey done on various image forgery detection techniques and finally conclude the comparative study with some parameters. Also tools are mentioned to detect the forged images that travel over the network or by natural way for daily forensics, image processing, and security. Salam A.Thajeel [4], discussed digital image forensics and its types, challenges and research problems and detail analysis of the existing approaches for detect image tampering. Author also discussed block based method and key point-based method and popular techniques of two methods. Moreover, most of the methods may not address the problems. Therefore, there is a need to develop techniques that is efficient to deal with these challenges. The Speeded Up Robust Features (SURF) [6] were applied to extract features instead of SIFT. However, although these methods can locate the matched key-points, most of them cannot locate the forgery regions very well; therefore, they cannot achieve satisfactory detection results and, at the same time, a sustained high recall rate [5]. A novel copy– move forgery detection scheme using

This work presents the image forgery detection based on Scale Invariant Feature Transform (SIFT) which finally all extracted features will give the final result. In this work we presented a novel technique for unsupervised forensic analysis of image file containers. To achieve the forgery detection in the image file content, defined by different manufacturers, models and software processing. Will proposed the first formal approach to perform integrity verification and difference identification and classification based on such features. Our outcome will demonstrates that the proposed strategy will be very effective in detecting image forgery and its accuracy will be acceptable compared to the other techniques

Abstract- Now-a-days, it is very easy to manipulate an image by adding or removing some features in an image without leaving any clue of editing the original image. They use advanced tools to digitally manipulate images to create forged image without finding a clue on it to find the forged region of an originality of images. These modifications are not visible when we see it into the naked eye. Splicing and Copy-move forgeries are most common forgery techniques. In splicing forgery, a small region in one image is cut and paste over an another image. Whereas in copy-move, a small region of an image is copied and pasted over a same image. The devices like cameras are getting more and more digitized, there is an increase in the need for digital image authentication, validation and forgery detection. This paper has an approach for the Splicing and Copy-move forgery detection. Copy-move and Splicing are the passive image forgery detection techniques. Initially, an image is taken as an input for both copy-move and splicing forgery. For both copy-move and Splicing detection, pre-processing and enhanced threshold methods are used to extract the features in an image. After feature extraction, using SVM we find whether it is authentic or forged by using RBF. If the given input image is authentic then the output will be the Black screen(No forged region). When the SVM identified it is forged, then using PCA algorithm we remove the authentic region and shows only the forged region as an output.

results in case of image blurring and video frame reconstruction applications. DWT is wavelet transform for which the wavelets are discretely sampled. An approximation to DWT is used for data compression if signal is already sampled. It is an efficient approach to lossless compression. As compared to above methods, DWT has numerous drawbacks. Unlike PCA, DWT has high cost of computing. In contrast to DCT, DWT has certain limitations like signal blurring, ringing noise near edge regions in images or video frames, longer compression time, lower quality than JPEG at low compression rates etc. Another method called Singular Value Decomposition (SVD) is being increasingly used for tampering detection. SVD is a very robust technique. The technique involves refactoring of given digital image in three different feature based matrices. The small set called singular values preserve the useful features of the original image. The advantages of SVD include lesser memory requirement. It has many applications in data analysis, signal processing, pattern recognition, image compression, noise reduction, image blurring, face recognition, forensics, embedding watermarking to an image. Some techniques used Fourier-Mellin Transform (FMT) to obtain features. Keypoint-based methods

There are various forgery detecting techniques have been invented so far. Monga [1] developed a process that includes first feature extraction (image hash) and second is coding of the hash result to form the final resultant hash. Image hashing methods are either global or local depending on requirement. Global features not sensitive to tampering in small areas in the image at the same time hashes are short, while local features can detect possible regional modifications but usually produce longer hashes as compared to global feature. In [2], Xiang et al. used a method which is using image histogram invariance to geometric deformations. It is strong to geometric attacks such as scaling, but images with similar histograms and different contents distinguishing are not possible here. Tang et al. [3] propose a forgery detection method using nonnegative matrix factorization (NMF). The given input image is first converted into a fixed-sized array of pixel. A next image is created by pixels rearranging and applying Non Negative matrix Factorization (NMF) to produce a final feature matrix, which is then quantized. The generated hash string is encrypted to generate the final image hash. Swaminathan et al. [4] proposed hashing technique based on rotation invariance using Fourier-Mellin transform. Proposed method is robust to various content-preserving manipulations such as jpeg compression geometric distortions such as filtering operations, scaling. Lei.et al. [5] proposed a method in which the DFT(Discrete Fourier Transform) of random transform coefficients find first and then quantization of the DFT coefficients to form image hash for image content authentication take place . The proposed algorithm can withstand almost all the important image processing manipulations, including blur, JPEG compression, addition of noise

questioned, because of the easiness with which these images can be changed in both its origin & content as a result of tremendous growth of digital image editing software. Digital image investigation is the latest research field which intends to authorize the authenticity of images. There are various methods proposed in digital forensics in recent years. Passive digital image tampering detection is one of it, which aims at verifying the authenticity of digital images without any a prior knowledge on the original images. A copy-move forgery detection is one of the passive technique which is created by copying and pasting content within the original image, and potentially post-processing it. In this paper, we use 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 Shift vector concept and for each shift vector a counter is incremented as many times as the same shift vector is computed, we can locate the copy move region in the image.

human life more comfortable and secure, but the security to the original documents belongs to the authenticated person is remained as concerned in the digital image processing domain. A new study is proposed in this research paper to detect the forgery detection in accurate manner using the adaptive over-segmentation and feature point matching. The integration of the block-based and key point-based forgery detection methods is the key idea in the proposed study and the detection of the suspected regions are detected by the adaptive non-overlapping and irregular blocks and this process is carried out using the adaptive over-segmentation algorithm. The extraction of the feature points is performed by performing the matching between the each block and its features. The feature points are gradually replaced by using the super pixels in the proposed Forgery Region Extraction algorithm and then merge the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions; finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The proposed forgery detection algorithm achieves much better detection results even under various challenging conditions the earlier methods in all aspects. We will analyze the results obtained by the both SIFT and SURF and it is proved that the proposed technique SURF is giving more satisfactory results by both subjective and objective analysis.

T. Blaschke et al,” Object based image analysis for remote sensing,” 2009 In this paper [6] they clarified that there is additionally a need of environmental monitoring which should be possible through the image receivables from specific areas. These images can give information which is further helpful for exploratory purposes. The moderating of certain characteristic things, the maintainability variable, preservation objectives all such can be further made with the assistance of the specific image information. The Object-based analysis is being utilized now which is another technique and can be useful to give the data required. The extensive pixel images give considerably more information which is much for informatory. The extraction of image data is utilized for spatial arranging. This data can likewise be utilized for monitoring programs. The data that is gotten can grow significantly more changes which can be useful for different fields moreover. Such changes that are should be made can be watched additionally from far distances and can be overviewed every once in a while.

When creating a digital composite, it is very difficult to match the lighting conditions from the individual photographs. Therefore lighting inconsistencies can be used as a tool for revealing digital forgeries. M. Johnson and H. Farid proposed a technique for estimating the direction (within one degree of freedom) of an illuminating light source. Because the right side of the face in fig 1(a) is more illuminated than the left, we can infer that a light source is positioned to the right. This observation can be formalized by making simplifying assumptions: the amount of light striking a surface is proportional to the surface normal and the direction to a single light source. With knowledge of 3-D surface normals, the direction to the light source can therefore be estimated [2].Because 3-D surface normals usually cannot be determined from a single image, the authors in [1] consider only the 2-D surface normals at the occluding object boundary. In return, they estimate two of the three components of the light source direction. Although there remains an ambiguity in the estimated light direction, these two components of light direction are still useful in a forensic setting.