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image splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

Computers, Materials & Continua 2024年第1期78卷 795-810页

作者： Ala’a R.Al-Shamasneh Rabha W.Ibrahim Department of Computer Science College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science and Mathematics Lebanese American UniversityBeirut11022801Lebanon Department of Mathematics Mathematics Research CenterNear East UniversityNicosia99138Turkey Information and Communication Technology Research Group Scientific Research CenterAlayen UniversityDhi Qar64001Iraq

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research *** of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into *** were made to mitigate the effects of image splicing,which continues to be a significant research *** study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep *** stages make up the proposed *** first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced *** proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image *** proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery *** experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and ***,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature *** the regions or locations where image tampering has taken place is limited by the *** research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

关键词： image forgery image splicing deep learning Sonine functions

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image splicing Detection Based on Texture Features with Fractal Entropy

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Computers, Materials & Continua 2021年第12期69卷 3903-3915页

作者： Razi J.Al-Azawi Nadia M.G.Al-Saidi Hamid A.Jalab Rabha W.Ibrahim Dumitru Baleanu Department of Laser and Optoelectronics Engineering University of TechnologyBaghdad10066Iraq Department of Applied Sciences University of TechnologyBaghdad10066Iraq Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia IEEE:94086547 Kuala Lumpur59200Malaysia Department of Mathematics Cankaya UniversityBalgat06530AnkaraTurkey Institute of Space Sciences R76900 Magurele-BucharestRomania Department of Medical Research China Medical UniversityTaichung40402Taiwan

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more *** a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked ***,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered *** on detection of tampered images still carries great *** the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another *** proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and *** main advantage of FrEp is the ability to extract the texture information contained in the input ***,the“support vector machine”(SVM)classification is used to classify images into either spliced or *** analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection ***,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

关键词： Fractal entropy image splicing texture features LBP SVM

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splicing image and Its Localization:A Survey

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Journal of Information Hiding and Privacy Protection 2019年第2期1卷 77-86页

作者： Jinwei Wang Yangyang Li Department of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing University of Information Science and Technology Nanjing210044China

With the rapid development of information technology,digital images have become an important medium for information ***,manipulating images is becoming a common task with the powerful image editing tools and software,and people can tamper the images content without leaving any visible traces of splicing in order to gain personal *** are easily spliced and distributed,and the situation will be a great threat to social *** survey covers splicing image and its *** present status of splicing image localization approaches is discussed along with a recommendation for future research.

关键词： Social security image splicing image splicing localization

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