Image Forgery Detection Research Papers (original) (raw)

Image forgery detection becomes critical in the current environment since transfer of information now a day is through digital medium. Problem starts to appear as malicious users start to participate over the network. Most common type of forgery in such situation is copy and move. The proposed work uses the application of Gabor filter to detect and prevent copy move forgery and also predict the result in terms of accuracy.

- by and +1
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- Computer Science, Computer Engineering, Computer Networks, Image Forgery Detection

Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively,
highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

- by Engr Dr. Mubbashar Khatana
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- Computer Vision, Image Forgery Detection

With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. Now, you can add, change or delete significant information from an image, without leaving any visible signs of such tampering. With more than several millions pictures uploaded daily to the net, the move towards paperless workplaces, and the introduction of e-Government services everywhere, it is becoming important to develop robust detection methods to identify image tampering operations and validate the credibility of digital images. This led to major research efforts in image forensics for security applications with focus on image forgery detection and authentication. The study of such detection techniques is the main focus of this paper. In particular, we provide a comprehensive survey of different forgery detection techniques, complementing the limitations of existing reviews in the literature. The survey covers image copy-move forgery, splicing, forgery due to resampling, and the newly introduced class of algorithms, namely image retouching. We particularly discuss in detail the class of pixel-based techniques which are the most commonly used approaches, as these do not require any a priori information about the type of tampering. The paper can be seen as a major attempt to provide an up-to-date overview of the research work carried in this all-important field of multimedia.

- by Mohammad Qureshi and +1
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- Image Processing, Image Forgery Detection, Image Tampering Copy-Move Sift Image Forensics, Copy Move Forgery

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford's law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier in a short time.

- by arman parnak
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- Electronic Engineering, Artificial Intelligence, Image Processing, Machine Learning

A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs discriminative robust local binary patterns for encoding tampering traces and a support vector machine for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.

- by Engr Dr. Mubbashar Khatana
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- Image Forgery Detection

An essay on the theory of the difference between copies and originals.

- by James Elkins
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- Aesthetics, Art History, Art Theory, Contemporary Art
- by DrNoura Semary and +2
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- Image Processing, Image Forensic, Image Forgery Detection, Digital Image Processing

Using image manipulation programs has become easier and more powerful than before. Due to such fact, detection of image forgeries has produced significant interest recently. Falsification of images can initiate dangerous legal concerns. Among the most extensively utilized approaches for image forgeries is copy-move forgery in which a section of the image is copied and duplicated in another location in the same image. A significant part of a digital image can be covered or added using this procedure. In this paper, we propose an accurate algorithm for copy-move forgery detection. A block-based approach is suggested that uses the KD-tree data structure and a simple yet efficient feature vector to detect the possible forgery. The results demonstrated in this work match state of the art methods while providing a significant speedup.

Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visibile spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images. Specifically, we use cross-band co-occurrence matrices, in addition to spatial co-occurrence matrices, as input to a CNN model, which is trained to distinguish between real and synthetic faces. The results of our experiments confirm the goodness of our approach which outperforms a similar detection technique based on intra-band spatial co-occurrences only. The performance gain is particularly significant with regard to robustness against post-processing, like geometric transformations, filtering and contrast manipulations.

- by Ehsan Nowroozi
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- Image Processing, Machine Learning, Image Forensic, Image Forgery Detection

This online workshop will investigate the unhappy state of mind of those people in history who have been taken in by fakes and forgeries, as well as the afterlives of forged texts. What effect did the exposure and revelation of their... more

- by Stefan Bauer and +2
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- Philology, History, Art History, Journalism

—Digital imaging has grown to become the prevalent technology for creating, processing, and storing digital memory and proof. Though this technology brings many leverage, it can be used as a ambiguous tool for covering details and evidences. This is because today digital images can be tampered in such supremacy that forgery cannot be find visually. In fact, the immunity concern of digital content has arisen a long time ago and different methods to verify the efficiency of digital images have been developed. Digital images offer many features for forgery detection algorithm to take precedence of specifically the color and brightness of individual pixels as well as an image's resolution and format. These properties grant for analysis and similarity between the significance of digital forgeries in an attempt to develop an algorithm for detecting image tampering. This paper presents a technique for image copy or move image forgery detection using Radix Sort, FasterK-means clustering algorithm & DCT Keywords—Faster K-means clustering algorithm, DCT,Image forgery , Image forgery detection , Radix Sort .

- by IJFRCSCE Journal and +1
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- Image Processing, Image Forgery Detection, DCT, Image Forgery

This paper proposes a novel technique to assist the emergency vehicles namely ambulance to cross the traffic signal without any delay which arises due to the time taken by other vehicles to make a way for it. This is made possible by surveillance of the traffic starting from approximately 100-300 meters away from the traffic signal. Once an ambulance is detected, the signal is sent to the siren detector to identify whether ambulance is in an emergency state. The flow of traffic is then controlled by the Node MCU module.

with the fast growth of digital technology and user friendly image editing tools, duplicating the contents of digital images becomes easier. The main aim of a digital image forensic is to determine the originality of digital images. Copy move forgery is one of the common methods to forge the digital content. In this paper we introduce a passive method to identify the copy move forgery. It decomposes the image and extracts the moments as feature vector to detect the forgery. The experimental results are studied to demonstrate that the proposed technique detects the forgery and classify the related forged images as clusters based on hierarchical method by BIRCH algorithm. The authentic image even after the post processing operations done on images is identified. Computer vision related with automatic analysis and innovating the useful information from digital information is implemented. It is mainly applied in image understanding.

- by arman parnak
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- Engineering, Electronic Engineering, Artificial Intelligence, Image Processing

Secret communication techniques are of great demand since last 3000 years due to the need of information security and confidentiality at various levels of communication such as while communicating confidential personal data, medical data of patients, defence and intelligence information of countries, data related to examinations etc. With advancements in image processing research, Image encryption and Steganographic techniques have gained popularity over other forms of hidden communication techniques during the last few decades and a number of image encryption models are suggested by various researchers from time to time. In this paper, we are suggesting a new image encryption model based on Fibonacci and Lucas series.

- by Noura Semary
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- Computer Science, Forensics, Image Processing, Image Forensic
- by B. Mahdian and +1
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- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity
- by Pankaj Jindal
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- Image Forensic, Image Forgery Detection, Image manipulations

This system provides a method of automatically keeping water bowls full and refilling every time it is detected that they are not. This is highly useful for anyone who owns a pet, as it decreases the amount of work the owner will need to do. The system uses an AI model, trained with over a thousand images of water bowls. This allows it to accurately determine when a bowl needs filling. When an empty bowl is spotted, a subsystem consisting of a valve and other electronic parts releases stored water into the bowl. Through experimentation it has been shown the accuracy of the system is about 97% under optimal lighting conditions. Without a light source, the system does not function. Currently, the components are not of the highest quality and the system only works with the bowl used in testing. There are future plans to train the model with new pictures featuring an assortment of bowls. Additionally, an LED could be added to the system to solve the issue of it not working without external light.

Summary: The paper argues that the "Contract" is a modern fake (late 1990s) : its central piece, a drawing representing Pizarro a.o. conquistadors sailing to the Indies, allegedly by Guaman Poma, is merely a tracing based on the - retouched - 1936 facsimile of the Nueva corónica. Annotations supposedly in the hands of Blas Valera himself and his Jesuit colleague Gonzalo Ruíz, which match hands in other Miccinelli documents, indirectly prove their modern, forged origin, too. --- Contents: [I:] The Miccinelli Manuscripts: The biography of Blas Valera - The Miccinelli Conundrum 1995-2003 - Provenance issues - Selection of a Project - [II:] The Contract Sheet and its Symbolic Drawing: A Well-Hidden Manuscript - "The Drawing is Not a Tracing" - A Tracing After All - Identifying an Original and a Copy - The Missing Link - Select Evidence. [III:] Remaining Textual Elements on the Contract Sheet. [IV:] Conclusion. Notes. Works Cited.

- by Ivan Boserup
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- Image Forgery Detection, Literary Forgeries, Guaman Poma, Blas Valera

Digital Images are the reliable means of communicating visual information. It finds a wide range of application in our day-to-day life such as evidence. Throughout our day, what we come across almost all the time are images. In today’s sophisticated world of advanced technologies, the reliability of these digital images has been put into question. This is because of the widely available image processing software’s that even a novice tampers and creates a synthetic image, counterfeiting both its origin and content. Moreover the technology advent has also led to these forgeries difficult to distinguish from the authentic photographs. It incorporates a skilful tampering of images whereby, deceiving the viewers and avoiding further suspicion. Thus a number of digital image forensic techniques have been developed to verify the authenticity of digital images. This paper gives an idea on the digital image forensics and a survey specially focusing on the Block based copy move forgery detection method.

- by Admin ASDF
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- Computer Science, Image Forgery Detection, Computer Security, Cryptography

Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology. Digital videos have become very vital in court of law and media (print, electronic and social). On the other hand, a widely-spread availability of Video Editing Tools (VETs) have made video tampering very easy. Detection of this tampering is very important, because it may affect the understanding and interpretation of video contents. Existing techniques used for detection of forgery in video contents can be broadly categorized into active and passive. In this research a passive technique for video tampering detection in spatial domain is proposed. The technique comprises of two phases: 1) Extraction of features with proposed Video Binary Pattern (VBP) descriptor, and 2) Extreme Learning Machine (ELM) based classification. Experimental results on different datasets reveal that the proposed technique achieved accuracy 98.47%.

- by Engr Dr. Mubbashar Khatana and +1
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- Image Forgery Detection, Image Tampering

- by anuradha aatri
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- Computer Science, Computer Engineering, Computer Networks, Image Forgery Detection

Digital videos are an incredibly important source of information, and as evidence, they are highly inculpatory. Digital videos are also inherently prone to conscious semantic manipulations, such as copy–paste forgeries, which involve insertion or removal of objects into or from a set of frames. Such forgeries involve direct manipulation of the information presented by a video scene, thus having an immediate effect on the meaning conveyed by that scene. Given the highly influential nature of video data and the fact that they are easy to manipulate, it becomes important to devise measures that can help ascertain their integrity and authenticity, so that we can be certain of their ability to serve as reliable evidence. The challenge of detecting copy–paste forgeries in digital videos has been at the receiving end of much innovation over the last decade, and as a result, the available literature in this domain has grown to considerable proportions. However, thorough analysis of this literature appears to show that the task of detecting such forgeries necessitates the use of elaborate and operationally restrictive procedures, and somehow cannot be accomplished via a relatively simpler process, whose method of operation imposes little to no restrictions on its scope of applicability. With the aim of quashing this notion, in this paper, we present two simple forensic solutions that can enable an analyst to detect copy–paste forgeries quickly and effectively, without having to resort to any complicated analyses or relying on unrealistic presumptions. These solutions are based on optical flow inconsistency analysis and pattern noise abnormality analysis , and have been validated on a substantial set of realistically tampered test videos in a diverse experimental set-up, which is representative of a neutral testing platform and simulates a real-world heterogeneous forensic environment, where the analyst has no control over any of the variable parameters of the video creation or manipulation process. When tested in such an experimental set-up, the proposed solutions achieved an average accuracy rate of 98% and demonstrated attributes desired of an efficacious and practical forensic solution, all the while validating our initial hypothesis that not only can the task of copy–paste detection be accomplished in a fast and uncomplicated manner, but also that in an actual forgery scenario, the less onerous a forensic solution is, the more likely it is to succeed.

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips. This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers. The MR layer highlights the tampering traces by aggregation of frames. The CNN layers encode these tampering traces and are learned using transfer learning. Finally, parasitic layers classify the video clip (VC) as authentic or tampered. The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy. Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy. Comparative analysis shows that the proposed method outperforms the state-of-the-art methods. INDEX TERMS Spatial forgery detection, motion residual, deep learning, extreme learning machine, parasitic learning.

Abstract—Image forgery detection and its accuracy are addressed in the proposed work. The image authentication process aims
at finding the originality of an image. Due to the advent of many image editing software image tampering has become common.
The Enhanced hashing approach is suggested for image authentication. The concept of Hashing has been used for searching
images from large databases. It can also be applied to image authentication as it produces different results with respect to the
change in image. But the hashing methods used for similarity searches cannot be used for image authentication since they are no
sensitive for small changes. Moreover, we need a system that detects only perceptual changes. A new hashing method, namely,
enhanced robust hashing is proposed for image authentication, which uses global and local properties of an image. This method is
developed for detecting image forgery, including removal, insertion, and replacement of objects, and abnormal color
modification, and for locating the forged area. The local models include position and texture information of object regions in the image. The hash mechanism uses secret keys for encryption and decryption. IP tracing is done to track the suspicious nodes.

- by IJTET Journal
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- Image Forgery Detection, Perceptual Hashing, Image Authentication, Image Hashing
- by Mohammad Akbarpour Sekeh
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- Image Forensic, Image Forgery Detection, Copy Move Forgery
- by Mohd Foad Rohani
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- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity

Copy-move forgery is a type of image that is most commonly used. This technique is easy to use by many people. This, application can be used to detect copy-move forgery in digital image. The application convert the input image from RGB to graysacle. Then apply Discreate Cosine Transform method to perform the image decomposition, and to extract the image features with the SIFT. Then feature extraction results are clustered using nearest neigbour and estimated by geometric transformation using RANSAC method. SIFT local features is better for some geometric transformation, such as rotation and scaling which used in this study. Result showed that this application is able to detect copy-move forgery in digital images with or without rotation and scaling attacks up to 100% with threshold in 0.10. 1. PENDAHULUAN Dengan kecanggihan teknologi sekarang ini menyebabkan citra digital dengan mudah dapat dimanipulasi. Manipulasi gambar dilakukan dengan cara menambahkan atau menghapus beberapa elemen dari gambar yang menghasilkan sejumlah pemalsuan citra yang tidak dapat diperhatikan oleh mata manusia. Hal ini juga didukung dengan tersedianya software-software editing gambar yang berteknologi canggih yang dapat digunakan oleh orang yang tidak profesional sekalipun. Pemalsuan citra copy-move adalah jenis pemalsuan citra yang paling umum digunakan karena tekniknya yang mudah dilakukan oleh banyak orang dengan cara bagian dari gambar itu sendiri disalin dan disisipkan ke bagian lain dari gambar yang sama. Discrete Cosine Transfrom (DCT) biasa digunakan untuk mengubah sebuah sinyal frekuensi dasar. Kelebihan Discrete Cosine Transfrom (DCT) menghitung kuantitas bit-bit image tempat pesan tersebut disembunyikan di dalamnya [1]. DCT juga invarian terhadap noise, compression, dan blur [2]. Scale Invariant Feature Transform (SIFT) adalah sebuah algoritma dalam computer vision untuk mendeteksi dan mendeskripsikan fitur lokal dalam gambar. SIFT memilki kelebihan dapat mendeteksi lebih banyak point fitur dibanding dengan metode SURF. SIFT juga digunakan untuk mengekstrasi fitur dari sebuah gambar yang invarian untuk skala dan rotasi [3]. Berdasarkan permasalahan dan penelitian terkait di atas, maka penelitian ini mengangkat tema deteksi pemalsuan copy-move pada citra digital dengan menggunakan kombinasi metode deteksi yaitu Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transfrom (SIFT). Dengan menggunakan kedua metode ini diharapkan penelitian ini dapat meningkatkan akurasi, mampu mendeteksi wilayah pemalsuan copy-move, dan mempersingkat waktu komputasi dalam mendeteksi pemalsuan copy-move pada citra digital. Adapun manfaat dilakukannya penelitian ini diharapkan dapat menjadi masukan yang memudahkan pengguna dalam mendeteksi keaslian suatu citra. 2. LANDASAN TEORI 2.1. Pemalsuan Citra Pemalsuan citra digital adalah citra yang telah mengalami manipulasi pada perubahan isi dan konteks [4]. Pemalsuan citra digital memiliki banyak kesamaan dengan pemalsuan foto konvensional. Namun, dibandingkan memanipulasi film atau negatif analog. Pemalsuan digital dilakukan dengan mengubah data digital yang dimiliki suatu citra. Program komputer seperti Adobe Photoshop, GIMP dan Corel Paint Shop telah membuat perubahan pada foto digital menjadi luar biasa mudah, didukung oleh

In this paper we present a new algorithm to detect digital image forgery based on cellular automata and data embedding in spatial domain. The original JPEG image will be partitioned into some regions. We use region-based segmentation to specifying the desired regions of interest from the input image. First we extract the visual attributes of the original image and achieve the statistical information for the selected region. Then we apply linear cellular automata rules to create a robust cipher key from these values. We embed the cipher key into the spatial domain to authenticate and validate the original image. The proposed algorithm is applied on 100 numbers of grayscale images (size 800 × 600). The results have demonstrated the robustness and stable time complexity of the proposed method.

- by Minati Mishra
- •
- Image Processing, Image Forgery Detection, Cryptography, Digital Image Processing

The multimedia applications are rapidly increasing. It is essential to ensure the authenticity of multimedia
components. The image is one of the integrated components of the multimedia. In this paper ,we desing a
model based on customized filter mask to ensure the authenticity of image that means the image forgery
detection based on customized filter mask. We have satisfactory results for our dataset.

Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed
a method targeting the detection of both steganography and seam-carved forgery in JPEG images.
We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched
image and the modified version. In realistic detection, the untouched image and themodified version
may not be obtained at the same time, and different JPEG images may have different neighboring joint density
features. By exploring the self-calibration under different shift recompressions, we propose calibrated
neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered
images from untouched ones. Our study shows that this approach has multiple promising applications
in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or
comparable detection performances with a much smaller feature set while detecting several JPEG-based
steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic
Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By
integrating calibrated neighboring density with spatial domain rich models that were originally designed for
steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery
from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery
detection together.

- by Qingzhong Liu
- •
- Image Forgery Detection, Steganography, Seam Carving, Steganalysis

— In this paper, a multi-resolution Weber law descriptors (WLD) based image forgery detection method is introduced. Due to the maturing of digital image processing techniques, there are many tools, which can edit an image easily without leaving obvious traces to the human eyes. So the authentication of digital images is an important issue in our life. The proposed multi-resolution WLD extracts the features from chrominance components, which can give more information that the human eyes cannot notice. A support vector machine is used for classification purpose. The experiments are conducted on a large image database designed for forgery detection. The experimental results show that the accuracy rate of the proposed method can reach up to 93.33 % with multi-resolution WLD descriptor on the chrominance space of the images.

Apart from robustness and accuracy of copy–paste image forgery detection, time complexity also plays an important role to evaluate the performance of the system. In this paper, the focus point is to improve time complexity of the block-matching algorithm. Hence, a coarse-to-fine approach is applied to propose an enhanced duplicated region detection model by using sequential block clustering. Clustering minimizes the search space in block matching. This significantly improves time complexity as it eliminates several extra block-comparing operations. We determine time complexity function of the proposed algorithm to measure the performance. The experimental results and mathematical analysis demonstrate that our proposed algorithm has more improvement in time complexity when the block size is small.

- by Mohd Foad Rohani
- •
- Digital Investigation, Image Forensic, Image Forgery Detection, Time Complexity
- by Sondos Magdy
- •
- Forensics, Image Processing, Image Forensic, Image Forgery Detection

Image forgery is nowadays widely used as digital images are easy to manipulate due to high availability of powerful image processing tools. It is possible to add or remove objects from an image without leaving any visible traces of tampering. This paper describes a method for detection of copy-paste manipulation on JPEG digital images. It is a type of image forgery in which a part of the image is copied to another location in the image with the intent to cover or add an important image object. The detection method was implemented through extracting and analyzing blocking artifact grids (BAGs), introduced by block processing during JPEG compression. Analysis was based on fact that BAGs usually mismatch after performing copy-paste operations. Proposed method was demonstrated on two doctored images.

- by Dijana Tralić
- •
- Image Forensic, Image Forgery Detection
- by Federico Condello
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- Image Forgery Detection, Forgery, Fakery, Fraud, Forgery, Artemidorus Papyrus
- by Noura Semary
- •
- Computer Science, Forensics, Image Processing, Image Forensic

Digital videos are an incredibly important source of information, and as evidence, they are highly inculpatory. Digital videos are also inherently prone to conscious semantic manipulations, such as copy–paste forgeries, which involve insertion or removal of objects into or from a set of frames. Such forgeries involve direct manipulation of the information presented by a video scene, thus having an immediate effect on the meaning conveyed by that scene. Given the highly influential nature of video data and the fact that they are easy to manipulate, it becomes important to devise measures that can help ascertain their integrity and authenticity, so that we can be certain of their ability to serve as reliable evidence. The challenge of detecting copy–paste forgeries in digital videos has been at the receiving end of much innovation over the last decade, and as a result, the available literature in this domain has grown to considerable proportions. However, thorough analysis of this literature appears to show that the task of detecting such forgeries necessitates the use of elaborate and operationally restrictive procedures, and somehow cannot be accomplished via a relatively simpler process, whose method of operation imposes little to no restrictions on its scope of applicability. With the aim of quashing this notion, in this paper, we present two simple forensic solutions that can enable an analyst to detect copy–paste forgeries quickly and effectively, without having to resort to any complicated analyses or relying on unrealistic presumptions. These solutions are based on optical flow inconsistency analysis and pattern noise abnormality analysis, and have been validated on a substantial set of realistically tampered test videos in a diverse experimental set-up, which is representative of a neutral testing platform and simulates a real-world heterogeneous forensic environment, where the analyst has no control over any of the variable parameters of the video creation or manipulation process. When tested in such an experimental set-up, the proposed solutions achieved an average accuracy rate of 98% and demonstrated attributes desired of an efficacious and practical forensic solution, all the while validating our initial hypothesis that not only can the task of copy–paste detection be accomplished in a fast and uncomplicated manner, but also that in an actual forgery scenario, the less onerous a forensic solution is, the more likely it is to succeed.

- by Naveen Aggarwal and +1
- •
- Image Forgery Detection, Digital Forensics, Optical Flow, Digital Image Forensic

In today's world of E-Commerce everything comes online like Music,E-Books, Shopping all most everything is online. If you are using some service or buying things online then you have to pay for that. For that you have to do Net Banking or you have to use Credit card which will do online payment for you. In today's environment when everything is online, the service you are using for E-Payment must be secure and you must protect your banking information like debit card or credit card information from possible threat of hacking. There were lots way to threat like Key logger, Forgery Detection, Phishing, Shoulder surfing. Therefore, we reveal our actual information of Bank and Credit Card then there will be a chance to lose data and same credit card and hackers can use banking information for malicious purpose. In this paper we discuss available E-Payment protocols, examine its advantages and delimitation's and shows that there are steel needs to design a more secure E-Payment protocol. The suggested protocol is based on using hash function and using dynamic or virtual password, which protects your banking or credit card information from possible threat of hacking when doing online transactions.

In todayÃ¢â‚¬â„¢s era role of digital image is eloquent. With its escalating importance and usage it becomes necessary to assess if the content is realistic or has been manipulated to trick the observer. Image forensics answers all these questions. Adding some useful features or eliminating awkward information is the extensively used tampering technique. The image forgery caused using this technique refers to cloning or copy-move attack. This paper elaborates the manifold techniques to detect cloned image forgery.

- by IJSRD Journal
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- Image Forgery Detection, Cloning, Image forensics, Tampering