Relevant bibliographies by topics / Image tamperings

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

Academic literature on the topic 'Image tamperings'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Image tamperings"

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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 (July 19, 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 study is to replace particular objects in an original image with other objects that are similar to another selected image. In this study, we try to replace the ball object in the original image with another ball object from another image
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Singhania, Shruti, Arju N.A, and Raina Singh. "Image Tampering Detection Using Convolutional Neural Network." International Journal of Synthetic Emotions 10, no. 1 (January 2019): 54–63. http://dx.doi.org/10.4018/ijse.2019010103.

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Pictures are considered the most reliable form of media in journalism, research work, investigations, and intelligence reporting. With the rapid growth of ever-advancing technology and free applications on smartphones, sharing and transferring images is widely spread, which requires authentication and reliability. Copy-move forgery is considered a common image tampering type, where a part of the image is superimposed with another image. Such a tampering process occurs without leaving any obvious visual traces. In this study, an image tampering detection method was proposed by exploiting a convolutional neural network (CNN) for extracting the discriminative features from images and detects whether an image has been forged or not. The results established that the optimal number of epochs is 50 epochs using AlexNet-based CNN for classification-based tampering detection, with a 91% accuracy.
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Agarwal, Ritu, and Mallika Pant. "Image tampering detection using genetic algorithm." MATEC Web of Conferences 277 (2019): 02026. http://dx.doi.org/10.1051/matecconf/201927702026.

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As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.
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Li, Su Ying. "Round Descriptor Based on SIFT Operator Copy and Paste Forensics." Advanced Materials Research 912-914 (April 2014): 1379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1379.

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Powerful digital cameras and image processing software, so that the image of tampering counterfeit technology is developing rapidly, the role of digital images to verify the authenticity and the beginning of forensics technology will become more important. For the most common digital image tampering copy and paste operation, this paper presents a round-based SIFT descriptor operator forensic image copy and paste algorithm that extracts the image to be detected circular feature vector descriptor, and using feature vectors copy and paste the image matching to detect and locate the area. Experiments show that the method of image rotation, zoom, blur, noise and other image processing operations are robust, able to quickly and effectively detect tampering in digital images traces copy and paste operations, and is able to copy and paste the region for accurate positioning.
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Zhan, Cai, Lu Leng, Chin-Chen Chang, and Ji-Hwei Horng. "Reversible Image Fragile Watermarking with Dual Tampering Detection." Electronics 13, no. 10 (May 11, 2024): 1884. http://dx.doi.org/10.3390/electronics13101884.

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The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved.
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Chang, Yu Hong. "Based on Digital Image Forensics Double JPEG Compression Algorithm." Advanced Materials Research 1044-1045 (October 2014): 1098–101. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1098.

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Means of digital image tampering diverse, new technical approach increasingly renovation, therefore, the detection of forged digital tampering issues are complex, and must not universal, once and for all solutions. In this paper, digital image tampering common practices through research, proposed a re-deposit operations for double JPEG image processing software compression testing technology method using image compression and decompression process lossy compression of images tamper with forensic, this method can play an effective role in the detection.
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Lee, Chin-Feng, Chin-Ting Yeh, Jau-Ji Shen, and Taeshik Shon. "Image Authentication and Restoration Using Block-Wise Variational Automatic Encoding and Generative Adversarial Networks." Electronics 12, no. 16 (August 10, 2023): 3402. http://dx.doi.org/10.3390/electronics12163402.

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The Internet is a conduit for vast quantities of digital data, with the transmission of images being especially prevalent due to the widespread use of social media. However, this popularity has led to an increase in security concerns such as image tampering and forgery. As a result, image authentication has become a critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing image authentication techniques using deep learning to combat various image tampering attacks. Nevertheless, image authentication techniques based on deep learning typically classify only specific types of tampering attacks and are unable to accurately detect tampered images or indicate the precise location of tampered areas. The paper introduces a novel image authentication framework that utilizes block-wise encoding through Variational Autoencoder and Generative Adversarial Network models. Additionally, the framework includes a classification mechanism to develop separate authentication models for different images. In the training phase, the image is first divided into blocks of the same size as training data. The goal is to enable the model to judge the authenticity of the image by blocks and to generate blocks similar to the original image blocks. In the verification phase, the input image can detect the authenticity of the image through the trained model, locate the exact position of the image tampering, and reconstruct the image to ensure the ownership.
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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 (October 17, 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 locating splicing tampering in an image, AttDAU-Net, was proposed. The proposed AttDAU-Net is basically a U-Net that incorporates the spatial rich model filtering, an attention mechanism, an ASPP module and a multitask learning framework, in order to capture more multi-scale information while enlarging the receptive field and improving the detection precision of image splicing tampering. The experimental results on the datasets of CASIA1 and CASIA2 showed promising performance metrics for the proposed model (-scores of 0.7736 and 0.6937, respectively), which were better than other state-of-the-art methods for comparison, demonstrating the feasibility and effectiveness of the proposed AttDAU-Net in locating image splicing tampering.
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Mire, Archana V., Sanjay B. Dhok, Narendra J. Mistry, and Prakash D. Porey. "Localization of Tampering Created with Facebook Images by Analyzing Block Factor Histogram Voting." International Journal of Digital Crime and Forensics 7, no. 4 (October 2015): 33–54. http://dx.doi.org/10.4018/ijdcf.2015100103.

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Facebook images get distributed within a fraction of a second, which hackers may tamper and redistribute on cyberspace. JPEG fingerprint based tampering detection techniques have major scope in tampering localization within standard JPEG images. The majority of these algorithms fails to detect tampering created using Facebook images. Facebook utilizes down-sampling followed by compression, which makes difficult to locate tampering created with these images. In this paper, the authors have proposed the tampering localization algorithm, which locates tampering created with the images downloaded from Facebook. The algorithm uses Factor Histogram of DCT coefficients at first 15 modes to find primary quantization steps. The image is divided into BXB overlapping blocks and each block is processed individually. Votes cast by these modes for conceivable tampering are collected at every pixel position and the ones above threshold are used to form different regions. High density voted region is proclaimed as tampered region.
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Thabit, Rasha, Jaffer Ali, and Doaa Subhi. "Tampering Reveal Technique for Iris Images." International Journal of Computer Networks and Communications Security 8, no. 6 (June 30, 2020): 46–51. http://dx.doi.org/10.47277/ijcncs/8(6)1.

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Nowadays, many biometric based security systems depend on the iris images for authentication because of their features and ease of use. However, storing and sharing these sensitive images through open access networks will expose them to tampering. In order to ensure the safety of the iris images, this paper presents a new tampering reveal technique based on watermarking in the transform domain. In the proposed technique, the iris region is selected and isolated using interactive segmentation process (ISA). The authentication bits have been generated from the iris region (IR) and embedded in the Slantlet transform coefficients of the remaining part of the iris image which has been named as non-iris region (NIR). The use of ISA ensures the intactness of IR because it has been excluded from the embedding process. Several experiments have been conducted to test the visual quality, capacity, payload, and the tampering reveal performance. The experiments proved the ability of the proposed technique to reveal and localize any tampering in IR, in addition, the difference between the original iris image and the watermarked iris image is imperceptible
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Dissertations / Theses on the topic "Image tamperings"

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Xin, Xing. "A Singular-Value-Based Semi-Fragile Watermarking Scheme for Image Content Authentication with Tampering Localization." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/645.

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This thesis presents a novel singular-value-based semi-fragile watermarking scheme for image content authentication with tampering localization. The proposed scheme first generates a secured watermark bit sequence by performing a logical "xor" operation on a content-based watermark and content-independent watermark, wherein the content-based watermark is generated by a singular-value-based watermark bit sequence that represents intrinsic algebraic image properties, and the content-independent watermark is generated by a private-key-based random watermark bit sequence. It next embeds the secure watermark in the approximation subband of each non-overlapping 4×4 block using the adaptive quantization method to generate the watermarked image. The image content authentication process starts with regenerating the secured watermark bit sequence following the same process mentioned in the secured watermark bit sequence generation. It then extracts a possibly embedded watermark using the parity of the quantization results from the probe image. Next, the authentication process constructs a binary error map, whose height and width are a quarter of those of the original image, using the absolute difference between the regenerated secured watermark and the extracted watermark. It finally computes two authentication measures (i.e., M1 and M2), with M1 measuring the overall similarity between the regenerated watermark and the extracted watermark, and M2 measuring the overall clustering level of the tampered error pixels. These two authentication measures are further seamlessly integrated in the authentication process to confirm the image content and localize any possible tampered areas. The extensive experimental results show that the proposed scheme outperforms four peer schemes and is capable of identifying intentional tampering, incidental modification, and localizing tampered regions.
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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. Cependant, ces réseaux sont entraînés sur des données préparées par des équipes de recherche, qui diffèrent largement des données réelles rencontrées en pratique. Cet écart réduit considérablement l'efficacité opérationnelle des détecteurs de manipulations d'images. Cette thèse vise précisément à améliorer l'efficacité des détecteurs de manipulation d'images dans un contexte pratique, en atténuant l'impact de ce décalage de données. Deux stratégies complémentaires sont explorées, toutes deux issues de la littérature en apprentissage automatique : 1. Créer des modèles capables d'apprendre à généraliser sur de nouvelles bases de données ou 2. Sélectionner, voire construire, des bases d'entraînement représentatives des images à examiner. Pour détecter des manipulations sur un grand nombre d'images non étiquetées, les stratégies d'adaptation de domaine cherchant à plonger les distributions d'entraînement et d'évaluation dans un espace latent où elles coïncident peuvent se révéler utiles. Néanmoins, on ne peut nier la faible efficacité opérationnelle de ces stratégies, étant donné qu'elles supposent un équilibre irréaliste entre images vraies et manipulées parmi les images à examiner. En plus de cette hypothèse problématique, les travaux de cette thèse montrent que ces stratégies ne fonctionnent que si la base d'entraînement guidant la détection est suffisamment proche de la base d'images sur laquelle on cherche à évaluer, une condition difficile à garantir pour un praticien. Généraliser sur un petit nombre d'images non étiquetées est encore plus difficile bien que plus réaliste. Dans la seconde partie de cette thèse, nous abordons ce scénario en examinant l'influence des opérations de développement d'images traditionnelles sur le phénomène de décalage de données en détection de manipulation d'images. Cela nous permet de formuler des stratégies pour sélectionner ou créer des bases d'entraînement adaptées à un petit nombre d'images. Notre contribution finale est une méthodologie qui exploite les propriétés statistiques des images pour construire des ensembles d'entraînement pertinents vis-à-vis des images à examiner. Cette approche réduit considérablement le problème du décalage de données et permet aux praticiens de développer des modèles sur mesure pour leur situation
Today, it is easier than ever to manipulate images for unethical purposes. This practice is therefore increasingly prevalent in social networks and advertising. Malicious users can for instance generate convincing deep fakes in a few seconds to lure a naive public. Alternatively, they can also communicate secretly hidding illegal information into images. Such abilities raise significant security concerns regarding misinformation and clandestine communications. The Forensics community thus actively collaborates with Law Enforcement Agencies worldwide to detect image manipulations. The most effective methodologies for image forensics rely heavily on convolutional neural networks meticulously trained on controlled databases. These databases are actually curated by researchers to serve specific purposes, resulting in a great disparity from the real-world datasets encountered by forensic practitioners. This data shift addresses a clear challenge for practitioners, hindering the effectiveness of standardized forensics models when applied in practical situations.Through this thesis, we aim to improve the efficiency of forensics models in practical settings, designing strategies to mitigate the impact of data shift. It starts by exploring literature on out-of-distribution generalization to find existing strategies already helping practitioners to make efficient forensic detectors in practice. Two main frameworks notably hold promise: the implementation of models inherently able to learn how to generalize on images coming from a new database, or the construction of a representative training base allowing forensics models to generalize effectively on scrutinized images. Both frameworks are covered in this manuscript. When faced with many unlabeled images to examine, domain adaptation strategies matching training and testing bases in latent spaces are designed to mitigate data shifts encountered by practitioners. Unfortunately, these strategies often fail in practice despite their theoretical efficiency, because they assume that scrutinized images are balanced, an assumption unrealistic for forensic analysts, as suspects might be for instance entirely innocent. Additionally, such strategies are tested typically assuming that an appropriate training set has been chosen from the beginning, to facilitate adaptation on the new distribution. Trying to generalize on a few images is more realistic but much more difficult by essence. We precisely deal with this scenario in the second part of this thesis, gaining a deeper understanding of data shifts in digital image forensics. Exploring the influence of traditional processing operations on the statistical properties of developed images, we formulate several strategies to select or create training databases relevant for a small amount of images under scrutiny. Our final contribution is a framework leveraging statistical properties of images to build relevant training sets for any testing set in image manipulation detection. This approach improves by far the generalization of classical steganalysis detectors on practical sets encountered by forensic analyst and can be extended to other forensic contexts
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Nyeem, Hussain Md Abu. "A digital watermarking framework with application to medical image security." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/74749/1/Hussain%20Md%20Abu_Nyeem_Thesis.pdf.

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Dealing with digital medical images is raising many new security problems with legal and ethical complexities for local archiving and distant medical services. These include image retention and fraud, distrust and invasion of privacy. This project was a significant step forward in developing a complete framework for systematically designing, analyzing, and applying digital watermarking, with a particular focus on medical image security. A formal generic watermarking model, three new attack models, and an efficient watermarking technique for medical images were developed. These outcomes contribute to standardizing future research in formal modeling and complete security and computational analysis of watermarking schemes.
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Diallo, Boubacar. "Mesure de l'intégrité d'une image : des modèles physiques aux modèles d'apprentissage profond." Thesis, Poitiers, 2020. http://www.theses.fr/2020POIT2293.

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Les images numériques sont devenues un outil de communication visuel puissant et efficace pour transmettre des messages, diffuser des idées et prouver des faits. L’apparition du smartphone avec une grande diversité de marques et de modèles facilite la création de nouveaux contenus visuels et leur diffusion dans les réseaux sociaux et les plateformes de partage d’images. Liés à ce phénomène de création et publication d'images et aidés par la disponibilité et la facilité d’utilisation des logiciels de manipulation d’images, de nombreux problèmes sont apparus allant de la diffusion de contenu illégal à la violation du droit d’auteur. La fiabilité des images numériques est remise en cause que ce soit pour de simples utilisateurs ou pour des professionnels experts tels que les tribunaux et les enquêteurs de police. Le phénomène des « fake news » est un exemple bien connu et répandu d’utilisation malveillante d’images numériques sur les réseaux.De nombreux chercheurs du domaine de la cybersécurité des images ont relevé les défis scientifiques liés aux manipulations des images. De nombreuses méthodes aux performances intéressantes ont été développées basées sur le traitement automatique des images et plus récemment l'adoption de l'apprentissage profond. Malgré la diversité des techniques proposées, certaines ne fonctionnent que pour certaines conditions spécifiques et restent vulnérables à des attaques malveillantes relativement simples. En effet, les images collectées sur Internet imposent de nombreuses contraintes aux algorithmes remettant en question de nombreuses techniques de vérification d’intégrité existantes. Il existe deux particularités principales à prendre en compte pour la détection d'une falsification : l’une est le manque d'informations sur l'acquisition de l'image d'origine, l'autre est la forte probabilité de transformations automatiques liées au partage de l'image telles que la compression avec pertes ou le redimensionnement.Dans cette thèse, nous sommes confrontés à plusieurs de ces défis liés à la cybersécurité des images notamment l’identification de modèles de caméra et la détection de falsification d’images. Après avoir passé en revue l'état de l'art du domaine, nous proposons une première méthode basée sur les données pour l’identification de modèles de caméra. Nous utilisons les techniques d’apprentissage profond basées sur les réseaux de neurones convolutifs (CNN) et développons une stratégie d’apprentissage prenant en compte la qualité des données d’entrée par rapport à la transformation appliquée. Une famille de réseaux CNN a été conçue pour apprendre les caractéristiques du modèle de caméra directement à partir d’une collection d’images subissant les mêmes transformations que celles couramment utilisées sur Internet. Notre intérêt s'est porté sur la compression avec pertes pour nos expérimentations, car c’est le type de post-traitement le plus utilisé sur Internet. L’approche proposée fournit donc une solution robuste face à la compression pour l’identification de modèles de caméra. Les performances obtenues par notre approche de détection de modèles de caméra sont également utilisées et adaptées pour la détection et la localisation de falsification d’images. Les performances obtenues soulignent la robustesse de nos propositions pour la classification de modèles de caméra et la détection de falsification d'images
Digital images have become a powerful and effective visual communication tool for delivering messages, diffusing ideas, and proving facts. The smartphone emergence with a wide variety of brands and models facilitates the creation of new visual content and its dissemination in social networks and image sharing platforms. Related to this phenomenon and helped by the availability and ease of use of image manipulation softwares, many issues have arisen ranging from the distribution of illegal content to copyright infringement. The reliability of digital images is questioned for common or expert users such as court or police investigators. A well known phenomenon and widespread examples are the "fake news" which oftenly include malicious use of digital images.Many researchers in the field of image forensic have taken up the scientific challenges associated with image manipulation. Many methods with interesting performances have been developed based on automatic image processing and more recently the adoption of deep learning. Despite the variety of techniques offered, performance are bound to specific conditions and remains vulnerable to relatively simple malicious attacks. Indeed, the images collected on the Internet impose many constraints on algorithms questioning many existing integrity verification techniques. There are two main peculiarities to be taken into account for the detection of a falsification: one is the lack of information on pristine image acquisition, the other is the high probability of automatic transformations linked to the image-sharing platforms such as lossy compression or resizing.In this thesis, we focus on several of these image forensic challenges including camera model identification and image tampering detection. After reviewing the state of the art in the field, we propose a first data-driven method for identifying camera models. We use deep learning techniques based on convolutional neural networks (CNNs) and develop a learning strategy considering the quality of the input data versus the applied transformation. A family of CNN networks has been designed to learn the characteristics of the camera model directly from a collection of images undergoing the same transformations as those commonly used on the Internet. Our interest focused on lossy compression for our experiments, because it is the most used type of post-processing on the Internet. The proposed approach, therefore, provides a robust solution to compression for camera model identification. The performance achieved by our camera model detection approach is also used and adapted for image tampering detection and localization. The performances obtained underline the robustness of our proposals for camera model identification and image forgery detection
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Hsiao, Dun-Yu. "Digital Image Tampering Synthesis and Identification." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2207200517502800.

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Zou, Chuang Lou, and 莊潤洲. "The Study of Image Tampering Detection." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/58825581948530138596.

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碩士
國立中正大學
資訊工程研究所
88
Nowadays, Internet has become more and more popular. However, images are most oftenly used in Internet. Images can be used as the trademarks of company symbolization, electronic commerce (EC), multimedia, etc. However, there are also some security issues of images. Among them, the illegal like illegal copy and modification of image is a popular security problem which oftenly occurs in our daily life. Therefore, how to protect the intellectual property of images is an important research topic. The thesis planes to use image authentication to protect the images. Based on the image authentication, the modified places of the images will be pointed out; thus the images can preserve the image integrity. This thesis proposes two new methods for image authentication. The first method uses the RSA signatures and quadtree structure to achieve the image integrity. Based on those digital signatures, we can claim the authorship of the image and detect efficiently whether the image has been modified or not. On the other hand, we use quadtree structure to organize digital signatures; thus the detection procedure will be more efficient. The traditional image authentication methods cannot allow JPEG lossy compression since the JPEG lossy compression may destroy the signatures embedded in images. However, JPEG lossy compression method is often required and popularly used everywhere. Thus, the JPEG lossy compression should be taken into consideration. To improve the traditional methods, we propose a new image authentication that not only can prevent images tampered with but also allow reasonable JPEG lossy compression. Our method will extract some significant DCT coefficients and set a compression tolerant range of them. An extracted DCT coefficient will be survived after the image is not further modified or lossily compressed.
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Hsiao, Dun-Yu, and 蕭敦育. "Digital Image Tampering Synthesis and Identification." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/11083859577614039919.

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碩士
國立臺灣大學
電信工程學研究所
93
Have you ever edited some unsatisfied digital photographs of yours? If the answer is yes, then you have made some digital tampering. With powerful computer and mighty software, seasoned users could turn digital media into what they want. The detection of digital tampering has become a crucial problem. In most of the time, digital tampering is not perceptible by human; however, some traces of digital tampering may be left in the media during the process. Based on this idea, several detection methods are proposed in this thesis to against various common digital tampering without any help of embedded information such as the well-known atermarking technique. Effectiveness and results will be presented in each method, robustness will also be discussed.
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Chen, Yi-Lei, and 陳以雷. "Tampering Detection in JPEG Images." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/43195323821600534654.

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碩士
國立清華大學
資訊工程學系
97
Since JPEG has been a popularly used image compression standard, tampering detection in JPEG images now plays an important role. Tampering on compressed images often involve recompression and tend to erase those tampering traces existed in uncompressed images. We could, however, try to discover new traces caused by recompression and use these traces to detect the recompression tampering. The artifacts introduced by lossy JPEG compression can be seen as an inherent signature for recompressed images. In this thesis, we first propose a robust tampered image detection approach by periodicity analysis with the compression artifacts both in spatial and DCT domain. To locate the forged regions, we then propose a forged regions localization method via quantization noise model and image restoration techniques. Finally, we conduct a series of experiments to demonstrate the validity of the proposed periodic features and quantization noise model, which all outperform the existing methods. Also, we show the effectiveness and feasibility of our forged regions localization method with proposed image restoration techniques.
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Chen, Wei-Yu, and 陳威宇. "A novel image tampering proof scheme using image heterogeneous channel." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/24686228296813647540.

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碩士
國立勤益科技大學
資訊工程系
102
In the paperless applications world, the document contents protection becomes more and more important. And, many documents contain owners’ secret assets. In this thesis, we use the PNG (portable network graphics) format images to illustrate our scheme. PNG format image contains basic channel and heterogeneous channel, named alpha channel. Alpha channel is used to define the degree of image transparency. We can use alpha channel to hide important information. We use the transparently fuzzy cognitive of human eyes to achieve secret information hiding and retrieving. Accordingly, the usage of the alpha channel to hide secret is a non-visible watermark technology. When a part content of the document is marked as important, we will use three colors, black, gray, and white, to parse the marked content. After ternarization, three values of the attributes will be calculated from the marked document image. In our experimental results, we show that our methods can valid and meaningful hiding secrets using alpha channel.
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李明倫. "Watermarking Scheme for Tampering Detection and Image Recovery." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/9469vm.

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Books on the topic "Image tamperings"

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Forensic Analysis of Digital Image Tampering. Storming Media, 2004.

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Book chapters on the topic "Image tamperings"

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Xu, Guanshuo, Jingyu Ye, and Yun-Qing Shi. "New Developments in Image Tampering Detection." In Digital-Forensics and Watermarking, 3–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19321-2_1.

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Manu, V. T., and B. M. Mehtre. "Review of Image Tampering Detection Techniques." In Cryptographic and Information Security, 723–44. Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2018. http://dx.doi.org/10.1201/9780429435461-24.

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Nair, S. Hridya, Kasthuri A. S. Nair, Niharika Padmanabhan, S. Remya, and Riya Ratnakaran. "Image Forgery and Image Tampering Detection Techniques: A Review." In Proceedings of Third International Conference on Sustainable Expert Systems, 159–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7874-6_13.

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Iakovidou, Chryssanthi, Symeon Papadopoulos, and Yiannis Kompatsiaris. "Knowledge-Based Fusion for Image Tampering Localization." In IFIP Advances in Information and Communication Technology, 177–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49161-1_16.

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Wang, Wei, Jing Dong, and Tieniu Tan. "A Survey of Passive Image Tampering Detection." In Digital Watermarking, 308–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03688-0_27.

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Asaad, Aras, and Sabah Jassim. "Topological Data Analysis for Image Tampering Detection." In Digital Forensics and Watermarking, 136–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64185-0_11.

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García-Ordás, Diego, Laura Fernández-Robles, Enrique Alegre, María Teresa García-Ordás, and Oscar García-Olalla. "Automatic Tampering Detection in Spliced Images with Different Compression Levels." In Pattern Recognition and Image Analysis, 416–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_49.

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Chen, Luyi, Shilin Wang, Shenghong Li, and Jianhua Li. "Countering Universal Image Tampering Detection with Histogram Restoration." In Digital Forensics and Watermaking, 282–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40099-5_23.

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Liu, Qingzhong, Andrew H. Sung, Zhongxue Chen, and Lei Chen. "Exposing Image Tampering with the Same Quantization Matrix." In Multimedia Data Mining and Analytics, 327–43. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_15.

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Ye, Kui, Xiaobo Sun, Jindong Xu, Jing Dong, and Tieniu Tan. "Influence Evaluation for Image Tampering Using Saliency Mechanism." In Communications in Computer and Information Science, 518–27. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7299-4_43.

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Conference papers on the topic "Image tamperings"

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Reddy, B. Ramasubba, M. Sunil Kumar, P. Neelima, C. Sushama, Vedala Naga Sailaja, and D. Ganesh. "Medical Image Tampering Detection using Deep Learning." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), 1480–85. IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722104.

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Zhao, Liyang, and Longfang Wen. "Improved YOLOv8 for substation image tampering detection." In 2024 3rd International Conference on Smart Grids and Energy Systems (SGES), 21–25. IEEE, 2024. https://doi.org/10.1109/sges63808.2024.10824170.

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Li, Zhenjiang, and Jingzhe Sun. "Image Tampering Detection in Bilingual Scenes Based on Semantic." In 2024 International Conference on Data Science and Network Security (ICDSNS), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691209.

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Li, Ruiyang, Jing Zhu, Xiaohu Luo, and Zhao Ma. "Image Tampering Detection Method Based on Multi-Feature Fusion." In 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), 345–56. IEEE, 2024. http://dx.doi.org/10.1109/qrs-c63300.2024.00051.

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Shi, Jiayi, Haihong E, Jianhua Liu, Tianyi Hu, Xiaodong Qiao, Junpeng Ding, and Jiayu Huang. "Hybrid Dual-Channel Input Image Tampering Detection for Scientific Papers." In 2024 11th International Conference on Behavioural and Social Computing (BESC), 1–6. IEEE, 2024. https://doi.org/10.1109/besc64747.2024.10780708.

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Ankilla, Mayank Reddy, Suraj Tirumani, and R. Siva. "Forgery and Tampering Detection for Images Using Deep Learning." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10675216.

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Calderon, Felix, Juan J. Flores, and Sergio Bravo-Solorio. "Watermarks based on Pyramidal Images for Tampering Image Self-Restoration." In 2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2022. http://dx.doi.org/10.1109/ropec55836.2022.10018582.

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Gaborini, Lorenzo, Paolo Bestagini, Simone Milani, Marco Tagliasacchi, and Stefano Tubaro. "Multi-Clue Image Tampering Localization." In 2014 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2014. http://dx.doi.org/10.1109/wifs.2014.7084315.

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Yi-Lei Chen and Chiou-Ting Hsu. "Image tampering detection by blocking periodicity analysis in JPEG compressed images." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665184.

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Swaminathan, Ashwin, Min Wu, and K. J. Ray Liu. "Image Tampering Identification using Blind Deconvolution." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312848.

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