16 research outputs found

    BLUR-INVARIANT COPY-MOVE FORGERY DETECTION TECHNIQUE WITH IMPROVED DETECTION ACCURACY UTILIZING SWT-SVD

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    With the increase in interchange of data, there is a growing necessity of security. Considering the volumes of digital data that is transmitted, they are in need to be secure. Among the many forms of tampering possible, one widespread technique is Copy Move Forgery (CMF). This forgery occurs when parts of the image are copied and duplicated elsewhere in the same image. There exist a number of algorithms to detect such a forgery in which the primary step involved is feature extraction. The feature extraction techniques employed must have lesser time and space complexity involved for an efficient and faster processing of media. Also, majority of the existing state of art techniques often tend to falsely match similar genuine objects as copy move forged during the detection process. To tackle these problems, the paper proposes a novel algorithm that recognizes a unique approach of using Hu’s Invariant Moments and Log-polar Transformations to reduce feature vector dimension to one feature per block simultaneously detecting CMF among genuine similar objects in an image. The qualitative and quantitative results obtained demonstrate the effectiveness of this algorithm

    Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

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    In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods

    Detection of copy-move forgery in digital images using different computer vision approaches

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    Image forgery detection approaches are many and varied, but they generally all serve the same objectives: detect and localize the forgery. Copy-move forgery detection (CMFD) is widely spread and must challenge approach. In this thesis, We first investigate the problems and the challenges of the existed algorithms to detect copy-move forgery in digital images and then we propose integrating multiple forensic strategies to overcome these problems and increase the efficiency of detecting and localizing forgery based on the same image input source. Test and evaluate our copy-move forgery detector algorithm presented the outcome that has been enhanced by various computer vision field techniques. Because digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy for forgers, we propose strategies and applications based on the PatchMatch algorithm and deep neural network learning (DNN). We further focus on the convolutional neural network (CNN) architecture approach in a generative adversarial network (GAN) and transfer learning environment. The F-measure score (FM), recall, precision, accuracy, and efficiency are calculated in the proposed algorithms and compared with a selection of literature algorithms using the same evaluation function in order to make a fair evaluation. The FM score achieves 0.98, with an efficiency rate exceeding 90.5% in most cases of active and passive forgery detection tasks, indicating that the proposed methods are highly robust. The output results show the high efficiency of detecting and localizing the forgery across different image formats for active and passive forgery detection. Therefore, the proposed methods in this research successfully overcome the main investigated issues in copy-move forgery detection as such: First, increase efficiency in copy-move forgery detection under a wide range of manipulation process to a copy-moved image. Second, detect and localized the copy-move forgery patches versus the pristine patches in the forged image. Finally, our experiments show the overall validation accuracy based on the proposed deep learning approach is 90%, according to the iteration limit. Further enhancement of the deep learning and learning transfer approach is recommended for future work

    Image quality and forgery detection copula-based algorithms

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    Copula functions are important tools to investigate dependence structure between random variables. There are many copulas such as: Gaussian, Marshall-Olkin, Clayton, and Frank copulas. Although, copulas have been used in finance, oceanography, and hydrology, they have been applied in limited applications in the image processing field. In this thesis, copulas are applied to calculate the mutual information of two images, which in turn is used to measure image quality of a targeted image and also used to detect copy-move forgery in images. The proposed algorithms introduce new alternatives for existing image quality assessment and forgery detection methods. These algorithms are easy to use and highly accurate. The results for our image quality assessment algorithm are comparable or better than those of established methods in the literature, while the results for our image forgery detection algorithm are accurate even after applying different manipulation and post-processing techniques on the forged images. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b214099

    Handbook of Digital Face Manipulation and Detection

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    This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

    Handbook of Digital Face Manipulation and Detection

    Get PDF
    This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

    Biometrics

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    Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

    Recent Application in Biometrics

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    In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

    Discrete Wavelet Transforms

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    The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications
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