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Journal articles on the topic 'Image splicing'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

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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Vaishnavi, D., T. S. Subashini, G. N. Balaji, and D. Mahalakshmi. "LBP and GLCM Based Image Forgery Recognition." International Journal of Engineering & Technology 7, no. 4.6 (2018): 217. http://dx.doi.org/10.14419/ijet.v7i4.6.20478.

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The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged i

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Zeng, Pingping, Lianhui Tong, Yaru Liang, Nanrun Zhou, and Jianhua Wu. "Multitask Image Splicing Tampering Detection Based on Attention Mechanism." Mathematics 10, no. 20 (2022): 3852. http://dx.doi.org/10.3390/math10203852.

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In today’s modern communication society, the authenticity of digital media has never been of such importance as it is now. In this aspect, the reliability of digital images is of paramount importance because images can be easily manipulated by means of sophisticated software, such as Photoshop. Splicing tampering is a commonly used photographic manipulation for modifying images. Detecting splicing tampering remains a challenging task in the area of image forensics. A new multitask model based on attention mechanism, densely connected network, Atrous Spatial Pyramid Pooling (ASPP) and U-Net for

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Alshwely, Mohammed Kassem, and Saad N. AlSaad. "Image splicing detection based on noise level approach." Al-Mustansiriyah Journal of Science 31, no. 4 (2020): 55. http://dx.doi.org/10.23851/mjs.v31i4.899.

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The rapid development in technology and the spread of editing image software has led to spread forgery in digital media. It is now not easy by just looking at an image to know whether the image is original or has been tampered. This article describes a new image splicing detection method based on noise level as a major feature to detect the tempered region. Principal Component Analysis (PCA) is exploited to estimate the noise of image and the K-means clustering for authentic and forged region classification. The proposed method adopts Columbia Uncompressed Image Splicing Dataset for evaluation

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Subramaniam, Jalab, Ibrahim, and Mohd Noor. "Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients." Symmetry 11, no. 11 (2019): 1392. http://dx.doi.org/10.3390/sym11111392.

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The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fr

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Ahir, Prof D., Sakshi Kapse, Sayali Mhaske, Tanuja Pansare, and Param Kalane. "Adversarial Learning for Constrained Image Splicing Detection and Localization." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4320–27. http://dx.doi.org/10.22214/ijraset.2024.62616.

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Abstract: In the era of digital media, the manipulation of images has become a significant concern, particularly on social media platforms. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to develop a robust system capable of recognizing and localizing spliced or tampered images. This research proposes a novel approach that leverages Convolutional Neural Networks (CNNs) and adversarial learningtechniques to enhance the detection and localization of image splicing. The system is designed to aid in distinguishing between authentic and manipulated images, there

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Xiu, Hongling, and Fengyun Yang. "Batch Processing of Remote Sensing Image Mosaic based on Python." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 208. http://dx.doi.org/10.3991/ijoe.v14i09.9226.

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In the process of remote sensing image processing, analysis and interpretation, it is usually necessary to combine several local images into a complete image. Aiming at the shortcoming of long and complicated process of conventional semi-automatic video stitching. In this paper, using the splicing method of pixels, based on the Python interface of ArcGIS 10.1 platform, the idea of programming language is introduced and batch mosaic of remote sensing images is realized. Through the comparison with the image processing software, it is found that this method can shorten the time of image mosaic a

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Jimmy, alexander Cortés Osorio, Andrés Chaves Osorio José, and David López Robayo Cristian. "Hybrid algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors." Revista Facultad de Ingeniería, Universidad de Antioquia, no. 105 (November 2, 2021): 111–21. https://doi.org/10.17533/udea.redin.20211165.

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Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of p

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Xu, Bo, Guangjie Liu, and Yuewei Dai. "Detecting Image Splicing Using Merged Features in Chroma Space." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/262356.

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Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detectio

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Amerini, Irene, Rudy Becarelli, Roberto Caldelli, and Matteo Casini. "A Feature-Based Forensic Procedure for Splicing Forgeries Detection." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/653164.

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Nowadays, determining if an image appeared somewhere on the web or in a magazine or is authentic or not has become crucial. Image forensics methods based on features have demonstrated so far to be very effective in detecting forgeries in which a portion of an image is cloned somewhere else onto the same image. Anyway such techniques cannot be adopted to deal with splicing attack, that is, when the image portion comes from another picture that then, usually, is not available anymore for an operation of feature match. In this paper, a procedure in which these techniques could also be employed wi

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Li, Jin, Hao Xing, Baixin Yang, et al. "P‐9.5: Research on Image Quality Improvement Methods for Large Screen Splicing Display." SID Symposium Digest of Technical Papers 55, S1 (2024): 1177–80. http://dx.doi.org/10.1002/sdtp.17313.

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Compared to integrated large screen displays, the splicing scheme that uses multiple display units for splicing has significant advantages in various aspects to achieve large screen displays. This article is based on MLCD (Mini LCD) display scheme that combines LCD and LED, and proposes various algorithms to improve the image quality of the splicing. It effectively solves the image quality problems that occur in MLCD display schemes, while taking into account the advantages of cost and maintenance, it can also fully leverage the image quality advantages. The splicing image quality improvement

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Ramirez-Rodriguez, Ana Elena, Rodrigo Eduardo Arevalo-Ancona, Hector Perez-Meana, Manuel Cedillo-Hernandez, and Mariko Nakano-Miyatake. "AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks." Applied Sciences 14, no. 13 (2024): 5545. http://dx.doi.org/10.3390/app14135545.

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The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two ident

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Pham, Nam Thanh, Jong-Weon Lee, and Chun-Su Park. "Structural Correlation Based Method for Image Forgery Classification and Localization." Applied Sciences 10, no. 13 (2020): 4458. http://dx.doi.org/10.3390/app10134458.

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In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted

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Siddiqi, Muhammad Hameed, Khurshed Asghar, Umar Draz, et al. "Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns." Security and Communication Networks 2021 (September 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/4270776.

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With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detect

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Mallick, Devjani, Mantasha Shaikh, Anuja Gulhane, and Tabassum Maktum. "Copy Move and Splicing Image Forgery Detection using CNN." ITM Web of Conferences 44 (2022): 03052. http://dx.doi.org/10.1051/itmconf/20224403052.

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The boom of digital images coupled with the development of approachable image manipulation software has made image tampering easier than ever. As a result, there is massive increase in number of forged or falsified images that represent incorrect or false information. Hence, the issue of image forgery has become a major concern and it must be addressed with appropriate solution. Throughout the years, various computer vision and deep learning solutions have emerged with a purpose to detect forgery in case of digital images. This paper presents a novel approach to detect copy move and splicing i

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Weir, Jonathan, Raymond Lau, and WeiQi Yan. "Digital Image Splicing Using Edges." International Journal of Digital Crime and Forensics 2, no. 4 (2010): 63–75. http://dx.doi.org/10.4018/jdcf.2010100105.

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In this paper, the authors splice together an image which has been split up on a piece of paper by using duplication detection. The nearest pieces are connected using edge searching and matching and the pieces that have graphics or textures are matched using the edge shape and intersection between the two near pieces. Thus, the initial step is to mark the direction of each piece and put the pieces that have straight edges to the initial position to determine the profile of the whole image. The other image pieces are then fixed into the corresponding position by using the edge information, i.e.

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Nirosha, Kandukuri. "Digital Image Forgery Detection Using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 456–65. https://doi.org/10.22214/ijraset.2025.67285.

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Digital images are a main source of shared information in social media. Digital image forgery has become a growing concern with the advancement of image editing tools, leading to the spread of misleading and manipulated content. Detecting such forgeries is crucial for ensuring the authenticity and reliability of digital images. Various digital image forgery detection techniques are tied to detecting only one type of forgery, such as image splicing or copy-move it is not applied in real life. To enhance digital image forgery detection using deep learning techniques via transfer learning is unco

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张, 喆葳. "Image Splicing Detection Based on Image Quality Metrics." Journal of Image and Signal Processing 07, no. 03 (2018): 128–35. http://dx.doi.org/10.12677/jisp.2018.73015.

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Guo, Ruyan, Baiyi Xiao, and Shilun Zhang. "Research of feature matching algorithm on panoramic mosaic." Applied and Computational Engineering 6, no. 1 (2023): 1235–43. http://dx.doi.org/10.54254/2755-2721/6/20230635.

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Focusing on the application problem according to feature matching and panoramic image stitching, comparing direct stitching and smoothing after feature matching algorithms. Through time performance, synthesis accuracy, and synthesis efficiency, comprehensively compared their effects of synthesizing panoramic images to find a better solution for panoramic splicing. In the study, there are three groups of image pairs with parallax and brightness contrast, then use SIFT algorithm to conduct feature point and matching feature point. The RANSAC algorithm obtains the homograph matrix and completes t

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Muhidin, Zumratul, Muh Nasirudin Karim, and Muhamad Masjun Efendi. "Analysis of Splicing Manipulation in Digital Images using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) Methods." Journal of Applied Informatics and Computing 8, no. 2 (2024): 408–12. https://doi.org/10.30871/jaic.v8i2.8540.

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In the digital age, image manipulation is common, often done before publication on social media. However, this can lead to negative impacts, including visual deception. This research aims to detect splicing type image manipulation using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) methods. The process starts with image decomposition using DyWT to obtain LL sub-images, followed by local feature extraction using SIFT. An application built on desktop-based Matlab source was developed to detect splicing forgery in digital images. The test used 20 images, this image

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Liu, Bo, and Chi-Man Pun. "Splicing Forgery Exposure in Digital Image by Detecting Noise Discrepancies." International Journal of Computer and Communication Engineering 4, no. 1 (2015): 33–38. http://dx.doi.org/10.7763/ijcce.2015.v4.378.

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Liu, Jin, Hefei Ling, Fuhao Zou, WeiQi Yan, and Zhengding Lu. "Digital Image Forensics Using Multi-Resolution Histograms." International Journal of Digital Crime and Forensics 2, no. 4 (2010): 37–50. http://dx.doi.org/10.4018/jdcf.2010100103.

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In this paper, the authors investigate the prospect of using multi-resolution histograms (MRH) in conjunction with digital image forensics, particularly in the detection of two kinds of copy-move manipulations, i.e., cloning and splicing. To the best of the authors’ knowledge, this is the first work that uses the same feature in both cloning and splicing forensics. The experimental results show the simplicity and efficiency of using MRH for the purpose of clone detection and splicing detection.

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Saha, Monica. "Forensic Technique for Forgery Detection and Localization in Digital Image." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2721–27. https://doi.org/10.22214/ijraset.2025.68495.

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Abstract: The widespread availability of advanced image editing software has transformed digital image forgery into an urgent issue in multimedia forensics. Traditional forgery methods—copy-move, splicing, and retouching—taint the authenticity of digital images, allowing malicious individuals to disseminate misinformation, tamper with legal evidence, and compromise digital trust. Forgery detection and localization are crucial for uses in cybersecurity, journalism, law enforcement, and digital forensics.This paper provides a systematic survey and comparative evaluation of the latest forensic me

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Yan, Rui, Yu Jiang, Chenhao Zhang, et al. "Gastrointestinal image stitching based on improved unsupervised algorithm." PLOS ONE 19, no. 9 (2024): e0310214. http://dx.doi.org/10.1371/journal.pone.0310214.

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Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reco

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Moghaddasi, Zahra, Hamid A. Jalab, Rafidah Md Noor, and Saeed Aghabozorgi. "Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/606570.

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Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorit

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Riadi, Imam, Abdul Fadlil, and Titi Sari. "Image Forensic for detecting Splicing Image with Distance Function." International Journal of Computer Applications 169, no. 5 (2017): 6–10. http://dx.doi.org/10.5120/ijca2017914729.

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Burvin, P. Sabeena, and J. Monica Esther. "Analysis of Digital Image Splicing Detection." IOSR Journal of Computer Engineering 16, no. 2 (2014): 10–13. http://dx.doi.org/10.9790/0661-162111013.

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Jun Hou, Haojie Shi, and Yan Cheng. "Image Splicing Detection by Border Features." International Journal of Advancements in Computing Technology 5, no. 9 (2013): 857–63. http://dx.doi.org/10.4156/ijact.vol5.issue9.102.

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Kobozieva, А. А., and B. G. Yenakiiev. "Method for image splicing forgery detection." Informatics and mathematical methods in simulation 14, no. 1-2 (2024): 24–36. http://dx.doi.org/10.15276/imms.v14.no1-2.24.

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Ahmed, Belal, T. Aaron Gulliver, and Saif alZahir. "Image splicing detection using mask-RCNN." Signal, Image and Video Processing 14, no. 5 (2020): 1035–42. http://dx.doi.org/10.1007/s11760-020-01636-0.

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Zhu, Ye, Xiaoqian Shen, Shikun Liu, Xiaoli Zhang, and Gang Yan. "Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network." Applied Sciences 11, no. 18 (2021): 8437. http://dx.doi.org/10.3390/app11188437.

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Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple

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Hamid, S., N. Z. Bawany, and S. Khan. "AcSIS: Authentication System Based on Image Splicing." Engineering, Technology & Applied Science Research 9, no. 5 (2019): 4808–12. http://dx.doi.org/10.48084/etasr.3060.

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Text-based passwords are widely used for the authentication of digital assets. Typically, password security and usability is a trade-off, i.e. easy-to-remember passwords have higher usability that makes them vulnerable to brute-force and dictionary attacks. Complex passwords have stronger security but poor usability. In order to strengthen the security in conjunction with the improved usability, we hereby propose a novel graphical authentication system. This system is a picture-based password scheme which comprises of the method of image splicing. Authentication data were collected from 33 dif

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Hamid, Soomaiya, Narmeen Zakaria Bawany, and Shahzeb Khan. "AcSIS: Authentication System Based on Image Splicing." Engineering, Technology & Applied Science Research 9, no. 5 (2019): 4808–12. https://doi.org/10.5281/zenodo.3510300.

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Text-based passwords are widely used for the authentication of digital assets. Typically, password security and usability is a trade-off, i.e. easy-to-remember passwords have higher usability that makes them vulnerable to brute-force and dictionary attacks. Complex passwords have stronger security but poor usability. In order to strengthen the security in conjunction with the improved usability, we hereby propose a novel graphical authentication system. This system is a picturebased password scheme which comprises of the method of image splicing. Authentication data were collected from 33 diff

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ZHANG, Wenbo, Weidong LIU, Le LI, Jiyu LI, Yanli LI, and Huifeng JIAO. "Underwater multi-frame target images mosaic method based on adaptive image enhancement." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 5 (2022): 997–1003. http://dx.doi.org/10.1051/jnwpu/20224050997.

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The severe attenuation and scattering of light in the water reduces the effective field of view of the underwater camera, making the scene information contained in a single image limited, and it is difficult to meet the application requirements of the large-scale underwater scenes. To solve this problem, an underwater multi-frame target images mosaic method based on adaptive image enhancement is proposed in this paper. Firstly, the image blur prior is used to achieve the adaptive enhancement of underwater images to suppress the blur and colour distortion of underwater images. Then, the feature

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Sun, Rishuang, Jinliang Xu, and Huan Zhang. "Panoramic UAV Image Mosaic Method and Its Application in Pavement Paving Temperature Monitoring." Coatings 13, no. 3 (2023): 528. http://dx.doi.org/10.3390/coatings13030528.

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The low-altitude technology of unmanned airborne infrared detection system is used to effectively monitor the temperature segregation in the paving stage and realize the temperature uniformity control of asphalt pavement construction. The image mosaic method can splice two images with overlapping areas together to form a panoramic image. In order to solve the problems of long time-consuming and low accuracy of aerial image mosaic algorithm, the low-temperature area of the whole pavement can be obtained quickly and accurately. In this paper, threshold segmentation technology is introduced to co

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Tang, Wenquan, Jianchao Hu, and Qiaohua Wang. "High-Throughput Online Visual Detection Method of Cracked Preserved Eggs Based on Deep Learning." Applied Sciences 12, no. 3 (2022): 952. http://dx.doi.org/10.3390/app12030952.

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Cracked preserved eggs can easily decay, emit a peculiar smell, and cause cross-infection. The identification of cracked preserved eggs during production suffers from low efficiency and high cost. This paper proposes an online detection and identification method of cracked preserved eggs to address this issue. First, the images of preserved eggs are collected online. Then, each collected image is cut into a single image of the preserved egg, and the images of different surfaces of the same preserved egg are respectively spliced by the sequential splicing scheme and the matrix splicing scheme.

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Zhang, Chao Wei. "Based on the Hamilton Improved Circle of Scraps Joining Together." Applied Mechanics and Materials 536-537 (April 2014): 245–48. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.245.

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By using Hamilton improved circle, transversely and longitudinally cut up paper join together. By scanning image and image extraction techniques, the images of shredded paper are obtained. The process of shredding splicing is mainly divided into two parts, the image preprocessing and edge matching. Then the images conver into grey value matrixes and binarization are implemented on the image with appropriate threshold. The images of shredded paper are classified into nineteen lines. With the shortest Euclidean distance between the edge of shredding and Hamilton improved circle, shredded paper i

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K Hebbar, Nagaveni, and Ashwini S Kunte. "TRANSFER LEARNING APPROACH FOR SPLICING AND COPY-MOVE IMAGE TAMPERING DETECTION." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2447–52. http://dx.doi.org/10.21917/ijivp.2021.0348.

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Image authentication before using in any security critical applications has become necessary as the image editing tools are increasing and are handy to use in today''s world. Images could be tampered in different ways, but a universal method is required to detect it. Deep learning has gained its importance because of its promising performance in many applications. In this paper a new framework for image tampering detection using Error Level Analysis (ELA) and Convolutional Neural Network (CNN) with transfer learning approach is proposed. In this method, the images are pre-processed using ELA t

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Asif Tisekar, Abbas. "“Image Forgery Detection Using Transfer Learning”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46928.

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Abstract: In today’s digital world, images are often shared and used as evidence in news, legal cases, and social media. However, it has become easier to manipulate images using editing tools, which can lead to false information and serious consequences. Detecting these changes, known as image forgery, is important to make sure images are trustworthy. Traditional methods for detecting image tampering often struggle with accuracy, especially when the edits are small or done carefully. These methods also require a lot of manual work and may not keep up with the fast-growing technology of image e

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Li, Zhongwang, Qi You, and Jun Sun. "A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization." Electronics 11, no. 10 (2022): 1607. http://dx.doi.org/10.3390/electronics11101607.

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The goal of image splicing localization is to detect the tampered area in an input image. Deep learning models have shown good performance in such a task, but are generally unable to detect the boundaries of the tampered area well. In this paper, we propose a novel deep learning model for image splicing localization that not only considers local image features, but also extracts global information of images by using a multi-scale guided learning strategy. In addition, the model integrates spatial and channel self-attention mechanisms to focus on extracting important features instead of restrai

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Abd, El-Latif Eman I., and Nour Eldeen Khalifa. "COVID-19 digital x-rays forgery classification model using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1821–27. https://doi.org/10.11591/ijai.v12.i4.pp1821-1827.

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Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulat

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