Relevant bibliographies by topics / Image Forgery Detection

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

Academic literature on the topic 'Image Forgery Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Image Forgery Detection"

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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 (April 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 based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.
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Hosny, Khalid M., Akram M. Mortda, Nabil A. Lashin, and Mostafa M. Fouda. "A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network." Applied Sciences 13, no. 3 (January 18, 2023): 1272. http://dx.doi.org/10.3390/app13031272.

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Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection.
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Naincy and Ashok Kumar Bathla. "Comparative Study and Survey on Copy Move Image Forgery Detection Approaches." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 6 (June 30, 2015): 33–38. http://dx.doi.org/10.53555/nncse.v2i6.445.

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Nowadays the demand of digital images in various application areas is increasing and thus it is becoming important to ensure the authenticity of images. Due to easy availability of various image editing tools, continuous manipulations are done to create fake or forged images. Although various techniques like copy-move, splicing, resampling etc. for image forgery are present but copy move image forgery has received significant attention these days. Thus the focus of this paper is on copy-move image forgery detection techniques. We have presented a review of commonly used copy move image forgery detection techniques and the comparison of same is also showed to evaluate their performance on basis of various parameters.
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Naincy and Ashok Kumar Bathla. "Comparative Study and Survey on Copy Move Image Forgery Detection Approaches." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 9 (September 30, 2015): 01–06. http://dx.doi.org/10.53555/nncse.v2i9.441.

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Nowadays the demand of digital images in various application areas is increasing and thus it is becoming important to ensure the authenticity of images. Due to easy availability of various image editing tools, continuous manipulations are done to create fake or forged images. Although various techniques like copy-move, splicing, resampling etc. for image forgery are present but copy move image forgery has received significant attention these days. Thus the focus of this paper is on copy-move image forgery detection techniques. We have presented a review of commonly used copy move image forgery detection techniques and the comparison of same is also showed to evaluate their performance on basis of various parameters.
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Gautam, Shikha, and Anand Singh Jalal. "An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties." International Journal of Computer Vision and Image Processing 8, no. 1 (January 2018): 92–101. http://dx.doi.org/10.4018/ijcvip.2018010106.

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Digital images are found everywhere from cell phones to the pages of online news sites. With the rapid growth of the Internet and the popularity of digital image capturing devices, images have become major source of information. Now-a-days fudge of images has become easy due to powerful advanced photo-editing software and high-resolution cameras. In this article, the authors present a method for detecting forgery, which is detected by estimating camera's intrinsic noise properties. Differences in noise parameters of the image are used as evidence of Image tampering. The method works in two steps. In the first step, the given image is classified as forge or non-forge. In the second step, the forged region in the image is detected. Results show that the proposed method outperforms the previous methods and shows a detection accuracy of 85.76%.
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Vaishnavi, D., D. Mahalakshmi, and Venkata Siva Rao Alapati. "Visual Feature Based Image Forgery Detection." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 86. http://dx.doi.org/10.14419/ijet.v7i4.6.20436.

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In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.
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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 image forgery using a Convolutional Neural Network (CNN) with three different models i.e. ELA (Error Level Analysis), VGG16 and VGG19. The proposed method applies the pre-processing technique to obtain the images at a particular compression rate. These images are then utilized to train the model and further the images are classified as authentic or forged. The paper also presents the experimental results of the proposed method and performance evaluation in terms of accuracy.
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Bi, Xiuli, Wuqing Yan, Bo Liu, Bin Xiao, Weisheng Li, and Xinbo Gao. "Self-Supervised Image Local Forgery Detection by JPEG Compression Trace." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 232–40. http://dx.doi.org/10.1609/aaai.v37i1.25095.

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For image local forgery detection, the existing methods require a large amount of labeled data for training, and most of them cannot detect multiple types of forgery simultaneously. In this paper, we firstly analyzed the JPEG compression traces which are mainly caused by different JPEG compression chains, and designed a trace extractor to learn such traces. Then, we utilized the trace extractor as the backbone and trained self-supervised to strengthen the discrimination ability of learned traces. With its benefits, regions with different JPEG compression chains can easily be distinguished within a forged image. Furthermore, our method does not rely on a large amount of training data, and even does not require any forged images for training. Experiments show that the proposed method can detect image local forgery on different datasets without re-training, and keep stable performance over various types of image local forgery.
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Farid, H. "Image forgery detection." IEEE Signal Processing Magazine 26, no. 2 (March 2009): 16–25. http://dx.doi.org/10.1109/msp.2008.931079.

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Clara Shanthi, G., and V. Cyril Raj. "A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 215. http://dx.doi.org/10.14419/ijet.v7i3.27.17879.

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Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.
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Dissertations / Theses on the topic "Image Forgery Detection"

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Lê, Thi Ai Nhàn. "Statistical Modeling for Detection of Digital Image Forgery." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0046.

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À l’ère du numérique, la crédibilité du contenu des images est une préoccupation majeure en raison de la popularité des outils d’édition faciles à utiliser et peu coûteux. Les images falsifiées peuvent être utilisées pour manipuler l’opinion publique lors des élections, commettre des fraudes et discréditer ou faire chanter des personnes. Face à cette situation préoccupante, nous développons dans cette thèse trois techniques efficaces basées sur (i) les traces de dématriçage (ii) les traces de compression JPEG, et (iii) les traces de rééchantillonnage pour détecter les images falsifiées et localiser les différents types de falsification. Bien que ces techniques soient différentes, elles fonctionnent sous l’hypothèse commune que les manipulations peuvent altérer certaines propriétés statistiques sous-jacentes des images naturelles. Un processus de détection en deux étapes a été adopté pour chaque technique de détection : (i) analyser et modéliser les caractéristiques statistiques des images authentiques et falsifiées, puis (ii) concevoir un détecteur statistique pour différencier les images falsifiées des images authentiques en estimant les changements dans leurs modèles. Diverses expérimentations numériques sur plusieurs ensembles de données de référence bien connus mettent en évidence la qualité des performances et la robustesse des techniques de détection proposées
In today’s digital age, the trustworthiness of image content is of great concern due to the dissemination of easy-to-use and low-cost image editing tools. Forged images can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Faced with such a serious situation, we develop in this doctoral project three versatile techniques based on (i) demosaicing traces (ii) JPEG compression traces, and (iii) resampling traces for detecting forged digital images and localizing various types of tampering therein. Although these techniques are different, they work under the common assumption that manipulations may alter some underlying statistical properties of natural images. A two-steps detection process has been adopted for every detection technique: (i) analyze and model statistical features of both the authentic and forged images associated with specific in-camera and/or post-camera traces, then (ii) design a statistical detector to differentiate between the authentic and forged images by estimating statistical changes in their models. Various numerical experiments on several well-known benchmark datasets highlight the performances and robustness of the proposed detection techniques
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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 splicing forgery detection algorithm works on low-resolution images and considered classification algorithms (classifiers). Methods: The research methods used in this research are Implementation and Experimentation. Implementation was used to answer the first research question i.e., to improve the detection rate in splicing forgery. Experimentation was used to answer the second research question. The results of the experiment were analyzed using statistical analysis to find out how the examined algorithm works on different image resolutions and on the considered classifiers. Results: One-tailed Wilcoxon signed rank test was conducted to compare which algorithm performs better, the T+ value obtained was less than To so the null hypothesis was rejected and the alternative hypothesis which states that Algorithm 2 (our enhanced version of the algorithm) performs better than Algorithm 1 (original algorithm), is accepted. Experiments were conducted and the accuracy of the algorithms in different cases were noted, ROC curves were plotted to obtain the AUC parameter. The accuracy, AUC parameters were used to determine the performance of the algorithms. Conclusions: After the results were analyzed using statistical analysis, we came to the conclusion that Algorithm 2 performs better than Algorithm 1 in detecting the forged images. It was also observed that Algorithm 1 improves its performance on low-resolution images when trained on original images and tested on images of different resolutions but, in the case of Algorithm 2, its performance is improved when trained and tested on images of the same resolution. There was not much variance in the performance of both of the algorithms on images of different resolution. Coming to the classifiers, Algorithm 1 improves its performance on linear SVM whereas Algorithm 2 improves its performance when using the simple tree classifier.
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Bhatnagar, Kunal, and Gustav Ekner. "Copy-move Image Forgery Detection with Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302507.

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Copy-move manipulation is a forgery method used on images where a small part is copied to another part. This thesis analyses the detection of copy-move forgeries with the help of Convolutional Neural Networks (CNN). The model used is utilizing an existing custom CNN layer to identify features useful for detecting manipulations. The model is trained and validated on data with different grades of manipulation to determine which combinations give the highest accuracy. The grades are determined by the copy-move size, ranging between 10% and 60% of the image size. The results show that training on images with more minor modifications generally gives better results than training on images with more considerable modifications, regardless of whether validated on small or large modified images. Also, it can be concluded that the particular convolutional layer, in general, is suitable for copy-move detection.
En copy-move manipulation är en förfalskningsmetod för bilder som går ut på att kopiera en liten del av en bild till en annan del. Den här rapporten analyserar detekteringen av copy-move-förfalskningar med hjälp av Convolutional Neural Networks (CNN). Modellen som används utnyttjar ett redan existerande CNN-lager skapat för att identifiera egenskaper i bilden användbara för detektering av bildmanipulation. Modellen är både tränad och validerad på data med olika grader av manipulation för att bestämma vilka kombinationer som ger högst träffsäkerhet. Skalan bestäms av storleken på copy-move-operationerna, med ett spann mellan 10% och 60% av bilden. Resultatet visar att träning med bilder med små modifikationer i allmänhet ger bättre resultat än att träna på bilder med större modifikationer, oavsett om valideringen skett på bilder av låg eller hög manipuleringsgrad. Det kan även konstateras att det särskilda CNN-lagret är lämpligt för detektering av copy-move-operationer.
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Li, Yuan Man. "SIFT-based image copy-move forgery detection and its adversarial attacks." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952093.

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Nguyen, Hoai phuong. "Certification de l'intégrité d'images numériques et de l'authenticité." Thesis, Reims, 2019. http://www.theses.fr/2019REIMS007/document.

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Avec l’avènement de l’informatique grand public et du réseau Internet, de nombreuses vidéos circulent un peu partout dans le monde. La falsification de ces supports est devenue une réalité incontournable, surtout dans le domaine de la cybercriminalité. Ces modifications peuvent être relativement anodines (retoucher l’apparence d’une personne pour lui enlever des imperfections cutanées), dérangeantes (faire disparaitre les défauts d’un objet) ou bien avoir de graves répercussions sociales (montage présentant la rencontre improbable de personnalités politiques). Ce projet s’inscrit dans le domaine de l’imagerie légale (digital forensics en anglais). Il s’agit de certifier que des images numériques sont saines ou bien falsifiées. La certification peut être envisagée comme une vérification de la conformité de l’image à tester en rapport à une référence possédée. Cette certification doit être la plus fiable possible car la preuve numérique de la falsification ne pourra être établie que si la méthode de détection employée fournit très peu de résultats erronés. Une image est composée de zones distinctes correspondantes à différentes portions de la scène (des individus, des objets, des paysages, etc.). La recherche d’une falsification consiste à vérifier si une zone suspecte est « physiquement cohérente » avec d’autres zones de l’image. Une façon fiable de définir cette cohérence consiste à se baser sur les « empreintes physiques » engendrées par le processus d’acquisition. Le premier caractère novateur de ce projet est la différenciation entre les notions de conformité et d’intégrité. Un support est dit conforme s’il respecte le modèle physique d’acquisition. Si certains des paramètres du modèle prennent des valeurs non autorisées, le support sera déclaré non-conforme. Le contrôle d’intégrité va plus loin. Il s’agit d’utiliser le test précédent pour vérifier si deux zones distinctes sont conformes à un modèle commun. Autrement dit, contrairement au contrôle de conformité qui s’intéresse au support dans son ensemble, le contrôle d’intégrité examine l’image zone par zone pour vérifier si deux zones sont mutuellement cohérentes, c’est-à-dire si la différence entre les paramètres caractérisant ces deux zones est cohérente avec la réalité physique du processus d’acquisition. L’autre caractère novateur du projet est la construction d’outils permettant de pouvoir calculer analytiquement les probabilités d’erreurs du détecteur de falsifications afin de fournir un critère quantitatif de décision. Aucune méthode ou outil actuels ne répondent à ces contraintes
Nowadays, with the advent of the Internet, the falsification of digital media such as digital images and video is a security issue that cannot be ignored. It is of vital importance to certify the conformity and the integrity of these media. This project, which is in the domain of digital forensics, is proposed to answer this problematic
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Stanton, Jamie Alyssa. "Detecting Image Forgery with Color Phenomenology." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572.

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Khayeat, Ali. "Copy-move forgery detection in digital images." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/107043/.

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The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result.
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Mahfoudi, Gaël. "Authentication of Digital Images and Videos." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0043.

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Les médias digitaux font partie de notre vie de tous les jours. Après des années de photojournalisme, nous nous sommes habitués à considérer ces médias comme des témoignages objectifs de la réalité. Cependant les logiciels de retouches d'images et de vidéos deviennent de plus en plus puissants et de plus en plus simples à utiliser, ce qui permet aux contrefacteurs de produire des images falsifiées d'une grande qualité. L'authenticité de ces médias ne peut donc plus être prise pour acquise. Récemment, de nouvelles régulations visant à lutter contre le blanchiment d'argent ont vu le jour. Ces régulations imposent notamment aux institutions financières de vérifier l'identité de leurs clients. Cette vérification est souvent effectuée de manière distantielle au travers d'un Système de Vérification d'Identité à Distance (SVID). Les médias digitaux sont centraux dans de tels systèmes, il est donc essentiel de pouvoir vérifier leurs authenticités. Cette thèse se concentre sur l'authentification des images et vidéos au sein d'un SVID. Suite à la définition formelle d'un tel système, les attaques probables à l'encontre de ceux-ci ont été identifiées. Nous nous sommes efforcés de comprendre les enjeux de ces différentes menaces afin de proposer des solutions adaptées. Nos approches sont basées sur des méthodes de traitement de l'image ou sur des modèles paramétriques. Nous avons aussi proposé de nouvelles bases de données afin d'encourager la recherche sur certains défis spécifiques encore peu étudiés
Digital media are parts of our day-to-day lives. With years of photojournalism, we have been used to consider them as an objective testimony of the truth. But images and video retouching software are becoming increasingly more powerful and easy to use and allow counterfeiters to produce highly realistic image forgery. Consequently, digital media authenticity should not be taken for granted any more. Recent Anti-Money Laundering (AML) relegation introduced the notion of Know Your Customer (KYC) which enforced financial institutions to verify their customer identity. Many institutions prefer to perform this verification remotely relying on a Remote Identity Verification (RIV) system. Such a system relies heavily on both digital images and videos. The authentication of those media is then essential. This thesis focuses on the authentication of images and videos in the context of a RIV system. After formally defining a RIV system, we studied the various attacks that a counterfeiter may perform against it. We attempt to understand the challenges of each of those threats to propose relevant solutions. Our approaches are based on both image processing methods and statistical tests. We also proposed new datasets to encourage research on challenges that are not yet well studied
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Wang, Chun-Wei, and 王駿瑋. "Image Forgery Detection Algorithms." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/43151794650301101647.

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博士
淡江大學
資訊工程學系博士班
99
In this thesis, we propose a method to detect copy-move forgery of images and two methods to detect resampling of images. To detect copy-move forgery of an image, the given image is divided into overlapping blocks of equal size, features for each block are then extracted and represented as a vector, all the extracted feature vectors are then sorted using a radix sort. The difference of the positions of every pair of adjacent feature vectors, called shift vector, in the sorting list is computed. The accumulated number for each of the shift vectors is evaluated. A large accumulated number is considered as possible presence of a duplicated region, and thus all the feature vectors corresponding to the shift vectors with large accumulated numbers are detected, whose corresponding blocks are then marked to form a tentative detected result. Finally the medium filtering and connected component analysis are performed on the tentative detected result to obtain the final result. For resampling detection, two detection methods are proposed. The former method was exact detection which includes three steps: first, we present an algorithm Resampling Matrix Construction (RMC) that automatically derives the resampling matrix for any given factor. Second, we show an algorithm that constructs a zeroing mask for the resampling by a factor with the support of the corresponding resampling matrix produced by the proposed algorithm Zeroing Mask Derivation (ZMD). Lastly, we propose an algorithm RD that detects resampling on images using the zeroing masks in a specific order. The latter is an improved version of exact detection to detect a much wider range of resampling factors by checking some periodic repetition with an approximation detection mechanism. The experimental results have demonstrated that the proposed methods are indeed effective and efficient.
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Cozzolino, Davide. "Image Forgery Detection and Localization." Tesi di dottorato, 2015. http://www.fedoa.unina.it/10175/1/cozzolino_davide_27.pdf.

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Con la continua diffusione di strumenti software semplici e potenti per editare le immagini, la contraffazione di immagini è diventata sempre più comune; contemporaneamente si è diffuso l'interesse per l'image forensic cioè quelle metodologie e algoritmi capaci di rilevare l'integrità dell'immagine. In letteratura sono stati proposti numerosi approcci per rilevare se un'immagine è stata contraffatta o per localizzare la contraffazione all'interno dell'immagine. Questi approcci principalmente si basano sul rilevamento della presenza, assenza o incongruenza di alcune tracce presenti tipicamente nelle immagini digitali.In particolare nella tesi verranno approfondite tre categorie di approcci: 1. gli approcci basati sul Photo Response Non-Uniformity noise(considerato l'impronta digitale delle macchine fotografiche), 2. gli approcci utilizzati per rilevare duplicazioni all'interno dell'immagine, 3. gli approcci basati su descrittori sintetici dell'immagine.
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Books on the topic "Image Forgery Detection"

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Soni, Badal, and Pradip K. Das. Image Copy-Move Forgery Detection. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9.

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Das, Pradip K., and Badal Soni. Image Copy-Move Forgery Detection: New Tools and Techniques. Springer Singapore Pte. Limited, 2022.

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Leadership, Management and Data Science: Forgery, Sensor Fusion, Forgery Detection, Scene, Multi Label, Sound, Satellite Image,TR6 Binary, Cat Dog, Multiclass, Unsupervised, Fusion, Cancer, Breast Cancer. Independently Published, 2022.

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Book chapters on the topic "Image Forgery Detection"

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Lin, Xiaodong. "Image Forgery Detection." In Introductory Computer Forensics, 507–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00581-8_20.

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Hel-Or, Hagit, and Ido Yerushalmy. "Camera-Based Image Forgery Detection." In Handbook of Digital Forensics of Multimedia Data and Devices, 522–71. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118705773.ch14.

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Vijayakumar, Preethi, Elizabeth Mathew, M. Gayathry Devi, P. T. Monisha, C. Anjali, and Jisha John. "Image Forgery Detection: A Review." In Inventive Computation and Information Technologies, 769–77. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7402-1_54.

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Soni, Badal, and Pradip K. Das. "Oriented FAST Rotated BRIEF and Trie-Based Efficient Copy-Move Forgery Detection Algorithm." In Image Copy-Move Forgery Detection, 101–29. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_8.

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Soni, Badal, and Pradip K. Das. "Summing Up." In Image Copy-Move Forgery Detection, 131–33. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_9.

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Soni, Badal, and Pradip K. Das. "Key-Points Based Enhanced CMFD System Using DBSCAN Clustering Algorithm." In Image Copy-Move Forgery Detection, 69–83. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_6.

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Soni, Badal, and Pradip K. Das. "Copy-Move Forgery Detection Using Local Binary Pattern Histogram Fourier Features." In Image Copy-Move Forgery Detection, 33–42. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_3.

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Soni, Badal, and Pradip K. Das. "Blur Invariant Block-Based CMFD System Using FWHT Features." In Image Copy-Move Forgery Detection, 43–50. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_4.

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Soni, Badal, and Pradip K. Das. "Introduction." In Image Copy-Move Forgery Detection, 1–10. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_1.

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Soni, Badal, and Pradip K. Das. "Geometric Transformation Invariant Improved Block-Based Copy-Move Forgery Detection." In Image Copy-Move Forgery Detection, 51–67. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_5.

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Conference papers on the topic "Image Forgery Detection"

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Narayan, Dipanshu, Himanshu, and Rishabh Kamal. "Image Forgery Detection." In 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023. http://dx.doi.org/10.1109/icdt57929.2023.10151341.

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Benhamza, Hiba, Abdelhamid Djeffal, and Abbas Cheddad. "Image forgery detection review." In 2021 International Conference on Information Systems and Advanced Technologies (ICISAT). IEEE, 2021. http://dx.doi.org/10.1109/icisat54145.2021.9678207.

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Kumar, Amit, Namita Tiwari, and Meenu Chawla. "Regularized CNN Model for Image Forgery Detection." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y573sx.

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Digital images play a very important role in different areas in the modern technological scenario. Changing and manipulating the content of the digital image is a very easy task by using powerful image editing tools. In today's technology environment, digital photographs serve a critical function in a variety of fields. Using advanced image editing tools, changing and rearranging the content of a digital image is a simple operation. It is now possible to add, edit, or remove essential aspects from an image despite leaving any perceptible alterations. In addition to determining if the picture is authentic or forged, the metadata of the image may be examined, however, metadata can be altered. In this example, the authors use Error Level Analysis on each picture and matching parameters for error rate analysis to detect images of modifications using Deep Learning on a dataset of a false image and real photos. This experiment shows that by running through 100 epochs, we obtain the best training accuracy of 99.17 % and 95.11 % of accuracy validating.
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James, Alisha, E. Bijolin Edwin, Anjana M. C, Angel Mary Abraham, and Harsha Johnson. "Image Forgery detection on cloud." In 2019 2nd International Conference on Signal Processing and Communication (ICSPC). IEEE, 2019. http://dx.doi.org/10.1109/icspc46172.2019.8976862.

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Battiato, Sebastiano. "Session details: Image forgery detection." In MM '10: ACM Multimedia Conference. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3258350.

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Patel, Meet, Kartikay Rane, Niyati Jain, Praneel Mhatre, and Shree Jaswal. "Image Forgery Detection using CNN." In 2023 3rd International Conference on Intelligent Technologies (CONIT). IEEE, 2023. http://dx.doi.org/10.1109/conit59222.2023.10205377.

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Shi, Zenan, Haipeng Chen, Long Chen, and Dong Zhang. "Discrepancy-Guided Reconstruction Learning for Image Forgery Detection." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/154.

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In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (\eg, noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors.
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Charpe, Jayshri, and Antara Bhattacharya. "Revealing image forgery through image manipulation detection." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342759.

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Kekre, H. B., D. Mishra, P. N. Halarnkar, P. Shende, and S. Gupta. "Digital image forgery detection using Image hashing." In 2013 International Conference on Advances in Technology and Engineering (ICATE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icadte.2013.6524736.

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Hrudya P, Lekha S. Nair, Adithya S.M, Reshma Unni, Vishnu Priya H, and Prabaharan Poornachandran. "Digital image forgery detection on artificially blurred images." In 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications (C2SPCA). IEEE, 2013. http://dx.doi.org/10.1109/c2spca.2013.6749392.

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