Relevant bibliographies by topics / Image splicing

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Image splicing'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Image splicing"

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Kadam, Kalyani Dhananjay, Swati Ahirrao, and Ketan Kotecha. "Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing." Data 6, no. 10 (2021): 102. http://dx.doi.org/10.3390/data6100102.

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Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Car
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Jalab, Hamid, Thamarai Subramaniam, Rabha Ibrahim, Hasan Kahtan, and Nurul Noor. "New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection." Entropy 21, no. 4 (2019): 371. http://dx.doi.org/10.3390/e21040371.

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Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detecti
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Hussien, Nadheer Younus, Rasha O. Mahmoud, and Hala Helmi Zayed. "Deep Learning on Digital Image Splicing Detection Using CFA Artifacts." International Journal of Sociotechnology and Knowledge Development 12, no. 2 (2020): 31–44. http://dx.doi.org/10.4018/ijskd.2020040102.

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Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images
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Gaffar, Achmad Fanany Onnilita, Supriadi Supriadi, Arief Bramanto Wicaksono Saputra, Rheo Malani, and Agusma Wajiansyah. "A Splicing Technique for Image Tampering using Morphological Operations." Signal and Image Processing Letters 1, no. 2 (2019): 36–45. http://dx.doi.org/10.31763/simple.v1i2.4.

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Image tampering is one part of the field of image editing or manipulation that changes certain parts of the graphic content of a given image. There are several techniques commonly used for image tampering, such as splicing, copy-move, retouching, etc. Splicing is a type of image tampering technique that combines two different images, replacing particular objects, skewing, rotation, etc. This study applies the splicing technique to image tampering using morphological operations. Morphology is a collection of image processing operations that process images based on their shape. The aim of this s
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Pham, Nam, Jong-Weon Lee, Goo-Rak Kwon, and Chun-Su Park. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry 11, no. 1 (2019): 83. http://dx.doi.org/10.3390/sym11010083.

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the
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Amalraj, Shankar. "Detection of Image Splicing Using CNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48640.

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Abstract—In the era of advanced digital imaging and widespread multimedia sharing, image forgery has become an increasingly significant concern. Among various types of image manipulations, splicing — where regions from multiple images are combined to create a forged image — is one of the most common and deceptive forms. Detecting such manipulations is critical for applications in digital forensics, media verification, and security. This paper proposes an effective approach for the detection of image splicing using Convolutional Neural Networks (CNN). The proposed model leverages the ability of
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Yu, Xiao Bo, Yun Feng Zhang, and Yue Gang Fu. "Automatic Splicing Technology in Image Splicing Processing." Advanced Materials Research 1014 (July 2014): 367–70. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.367.

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Automatic splicing technology is all important research field of image processing, and has become a research focusing on the computer vision and computer graphics,and has important practical value in the fields of image splicing processing, medical image analysis and so on.On the basis of a linear transition method, this paper presents an algorithm which realizes to diminish the seams in overlap region according to the content of scenes.This algorithm avoids manual intervention during the mosaic process.With the help of automatic splicing technology based on the overlapping areas linear transi
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Su, Hang. "Image splicing detection using integrated LBP and DCT features." Applied and Computational Engineering 101, no. 1 (2024): 71–78. http://dx.doi.org/10.54254/2755-2721/101/20240976.

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Abstract. Image splicing is one of the most common techniques used for picture manipulation and forgery. With the advent of user-friendly photo editing software, image splicing has become more prevalent and increasingly difficult to detect. This paper proposes a passive photo splicing detection approach based on Local Binary Patterns (LBP) and Discrete Cosine Transform (DCT) to identify splicing forgeries. The input RGB images are first converted to the YCbCr color space. Subsequently, the chrominance channels, Cb and Cr, are divided into overlapping blocks. Each block's LBP code is then trans
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Mushtaq, Saba. "A simple and fast method based on DWT and image texture for detection of splicing forgery in images." RESEARCH REVIEW International Journal of Multidisciplinary 4, no. 2 (2019): 332–38. https://doi.org/10.5281/zenodo.2574905.

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This paper proposes a simple and fast method for splicing detection in images based on discrete wavelet transform (DWT) and statistical texture features of the image. Splicing is a common image forgery operation involving merging of two different images to create a new image to conceal or change the information conveyed by the original images. The fact that splicing forgery introduces new texture into the original image in addition to sharp transitions and abrupt changes is exploited in the proposed method. Images are firstly subjected to 3 level DWT decomposition followed by image reconstruct
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Ciptasari, Rimba Whidiana, Kyung Hyune Rhee, and Kouichi Sakurai. "Exploiting reference images for image splicing verification." Digital Investigation 10, no. 3 (2013): 246–58. http://dx.doi.org/10.1016/j.diin.2013.06.014.

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Dissertations / Theses on the topic "Image splicing"

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Devagiri, Vishnu Manasa. "Splicing Forgery Detection and the Impact of Image Resolution." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14060.

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Context: There has been a rise in the usage of digital images these days. Digital images are being used in many areas like in medicine, wars, etc. As the images are being used to make many important decisions, it is necessary to know if the images used are clean or forged. In this thesis, we have considered the area of splicing forgery. In this thesis, we are also considering and analyzing the impact of low-resolution images on the considered algorithms. Objectives. Through this thesis, we try to improve the detection rate of splicing forgery detection. We also examine how the examined splicin
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Prinz, Gary S. "Effect of Beam Splicing on Seismic Response of Buckling-Restrained Braced Frames." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2126.pdf.

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Cun, Xiao Dong. "Image splicing localization via semi-global network and fully connected conditional random fields." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950634.

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Clark, Francis. "A computational study of gene structure and splicing in model eukaryote organisms /." St. Lucia, Qld, 2003. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17395.pdf.

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Gondlyala, Siddharth Rao. "Enhancing the JPEG Ghost Algorithm using Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20692.

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Background: With the boom in the internet space and social media platforms, a large number of images are being shared. With this rise and advancements in technology, many image editing tools have made their way to giving rise to digital image manipulation. Being able to differentiate a forged image is vital to avoid misinformation or misrepresentation. This study focuses on the splicing image forgery to localizes the forged region in the tampered image. Objectives: The main purpose of the thesis is to extend the capability of the JPEG Ghost model by localizing the tampering in the image. This
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Kvavle, Joshua Monroe. "A System Level Approach to D-Fiber Electric Field Sensing." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3155.pdf.

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Julliand, Thibault. "Automatic noise-based detection of splicing in digital images." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC2057.

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Dans cette thèse, nous proposons trois nouvelles méthodes d’imagerie médico-légale pour détecter l’épissage dans les images numériques en exploitant les statistiques de bruit d’image. Pour ce faire, nous introduisons un nouvel outil, l’histogramme de la densité du bruit, et son dérivé, l’histogramme de la contribution de la densité du bruit. Nos méthodes permettent la détection d’épissage sur les images brutes et JPEG. Bien que l’utilisation de différences de bruit pour détecter l’épissage ait déjà été faite plusieurs fois, la plupart des méthodes existantes ont tendance à mal fonctionner sur
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Abecidan, Rony. "Stratégies d'apprentissage robustes pour la détection de manipulation d'images." Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0025.

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Aujourd'hui, la manipulation d'images à des fins non éthiques est courante, notamment sur les réseaux sociaux et dans la publicité. Les utilisateurs malveillants peuvent par exemple créer des images synthétiques convaincantes pour tromper le public ou dissimuler des messages dans des images numériques, posant des risques pour la sécurité nationale. Les chercheurs en analyse forensique d'image travaillent donc avec les forces de l'ordre pour détecter ces manipulations. Les méthodes d'analyse forensique les plus avancées utilisent notamment des réseaux neuronaux convolutifs pour les détecter. Ce
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Heckler, Géraud. "Étude de l'effet d'un stress oxydant sur l'activité spliceosomale et sur les RNP à boîtes C/D et H/ACA." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0351.

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Au cours du processus de maturation des pré-ARN, l’épissage alternatif (EA) inclut ou exclut sélectivement des séquences introniques et exoniques spécifiques, aboutissant à partir du même pré-ARN à différentes isoformes. Des études récentes étendues à l’ensemble du génome estiment que 95% des gènes humains sont épissés de façon alternative. Aussi, il n’est pas surprenant que des stress cellulaires induisent des changements du profil global d’épissage. Notre analyse globale de transcriptomique a révélé des variations d’épissages significatives en réponse à un stress oxydant. Nous nous sommes in
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卓宥亦, Yu-Yi Cho, and 卓宥亦. "Image Splicing Detection Using Inconsistent Multi-scale Local Noise Variances." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/ygjchs.

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碩士<br>國立臺北科技大學<br>電機工程系所<br>101<br>Due to the rapid development of image editing software, it becomes easy to tamper a digital image and create a fake image. One of the common and simple ways of the digital image tampering is image splicing, which is to copy a region of an image to another image, and add the contents not belonging to the original image. Therefore, the authenticity of digital image has become an important issue. Since different sources of images are productions of different sensors and post-processing, they will contain different noise levels. Based on this fact, we propose
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Book chapters on the topic "Image splicing"

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Zhang, Zhen, Ying Zhou, Jiquan Kang, and Yuan Ren. "Study of Image Splicing Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87442-3_136.

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Lau, Raymond, Jonathan Weir, and WeiQi Yan. "Digital Image Splicing Using Edges." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_61.

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Qu, Zhenhua, Guoping Qiu, and Jiwu Huang. "Detect Digital Image Splicing with Visual Cues." In Information Hiding. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04431-1_18.

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Wang, Bo, and Xiangwei Kong. "Image Splicing Localization Based on Re-demosaicing." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_92.

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Zhao, Xudong, Jianhua Li, Shenghong Li, and Shilin Wang. "Detecting Digital Image Splicing in Chroma Spaces." In Digital Watermarking. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18405-5_2.

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Liu, Jin, Hefei Ling, Fuhao Zou, and Zhengding Lu. "Image Splicing Detection Using Multi-resolution Histogram." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_76.

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ur Rhhman, Hafiz, Muhammad Arif, Anwar Ullah, et al. "Comparative Analysis of Various Image Splicing Algorithms." In Soft Computing Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52190-5_15.

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Zhao, Xudong, Shilin Wang, Shenghong Li, Jianhua Li, and Xiang Lin. "A Distributed Scheme for Image Splicing Detection." In Digital-Forensics and Watermarking. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43886-2_23.

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Agarwal, Ritu, and Om Prakash Verma. "Image Splicing Detection Using Hybrid Feature Extraction." In Advances in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0942-8_62.

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Meena, Kunj Bihari, and Vipin Tyagi. "Image Splicing Forgery Detection Techniques: A Review." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_35.

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Conference papers on the topic "Image splicing"

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Jiang, Ningjing, Dengyong Zhang, and Feng Li. "Image Splicing Detection Based on Hybrid CNN-Transformer Network." In 2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT). IEEE, 2024. https://doi.org/10.1109/mvipit65697.2024.00049.

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Hosseini, Seyed Hesamoddin, Amene Vatanparast, and Amir Hossein Taherinia. "An Effective Hybrid Algorithm for Locating Splicing Forgery Image." In 2024 14th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2024. https://doi.org/10.1109/iccke65377.2024.10874562.

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Cao, Haokun, Yuanman Li, Xinyu Yang, and Xia Li. "Region Aware Framework for Constrained Image Splicing Detection and Localization." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849068.

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Abd Warif, Nor Bakiah, Muhammad Faiz Hamdan, Mohd Faizal Ab. Razak, Salwana Mohamad, and Ahmad Firdaus. "An Analysis of Image Splicing Detection using Convolutional Neural Network (CNN)." In 2024 1st International Conference on Cyber Security and Computing (CyberComp). IEEE, 2024. https://doi.org/10.1109/cybercomp60759.2024.10913841.

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Moghaddasi, Zahra, Hamid A. Jalab, and Rafidah Md Noor. "SVD-based image splicing detection." In 2014 International Conference on Information Technology and Multimedia (ICIMU). IEEE, 2014. http://dx.doi.org/10.1109/icimu.2014.7066598.

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Wei Wang, Jing Dong, and Tieniu Tan. "Effective image splicing detection based on image chroma." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413549.

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Mushtaq, Saba, and Ajaz Hussain Mir. "Novel method for image splicing detection." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968386.

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Li, Jie, and Zuling Wang. "Visual Transformer for Image Splicing Localization." In 2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2023. http://dx.doi.org/10.1109/icbase59196.2023.10303114.

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Manu, V. T., and B. M. Mehtre. "Visual artifacts based image splicing detection in uncompressed images." In 2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS). IEEE, 2015. http://dx.doi.org/10.1109/cgvis.2015.7449911.

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Vaishnavi, D., and T. S. Subashini. "Recognizing image splicing forgeries using histogram features." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2016. http://dx.doi.org/10.1109/icbdsc.2016.7460342.

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