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Bibliografias temáticas / Deep learning for Multimedia Forensics

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Literatura científica selecionada sobre o tema "Deep learning for Multimedia Forensics"

Autor: Grafiati

Publicado: 14 de março de 2023

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Artigos de revistas sobre o assunto "Deep learning for Multimedia Forensics"

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Amerini, Irene, Aris Anagnostopoulos, Luca Maiano, and Lorenzo Ricciardi Celsi. "Deep Learning for Multimedia Forensics." Foundations and Trends® in Computer Graphics and Vision 12, no. 4 (2021): 309–457. http://dx.doi.org/10.1561/0600000096.

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.., Ossama, and Mhmed Algrnaodi. "Deep Learning Fusion for Attack Detection in Internet of Things Communications." Fusion: Practice and Applications 9, no. 2 (2022): 27–47. http://dx.doi.org/10.54216/fpa.090203.

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The increasing deep learning techniques used in multimedia and networkIoT solve many problems and increase performance. Securing the deep learning models, multimedia, and networkIoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or networkIoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and networkIoT forens

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Celebi, Naciye Hafsa, Tze-Li Hsu, and Qingzhong Liu. "A comparison study to detect seam carving forgery in JPEG images with deep learning models." Journal of Surveillance, Security and Safety 3, no. 3 (2022): 88–100. http://dx.doi.org/10.20517/jsss.2022.02.

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Aim: Although deep learning has been applied in image forgery detection, to our knowledge, it still falls short of a comprehensive comparison study in detecting seam-carving images in multimedia forensics by comparing the popular deep learning models, which is addressed in this study. Methods: To investigate the performance in detecting seam-carving-based image forgery with popular deep learning models that were used in image forensics, we compared nine different deep learning models in detecting untouched JPEG images, seam-insertion images, and seam removal images (three-class classification)

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Hussain, Israr, Dostdar Hussain, Rashi Kohli, et al. "Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics." Mobile Information Systems 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2847580.

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Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The a

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Premanand Ghadekar, Vaibhavi Shetty, Prapti Maheshwari, Raj Shah, Anish Shaha, and Vaishnav Sonawane. "Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 01–14. http://dx.doi.org/10.46604/peti.2023.10290.

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Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a Res

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Ferreira, Sara, Mário Antunes, and Manuel E. Correia. "Exposing Manipulated Photos and Videos in Digital Forensics Analysis." Journal of Imaging 7, no. 7 (2021): 102. http://dx.doi.org/10.3390/jimaging7070102.

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Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to autom

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Parkhi, Abhinav, and Atish Khobragade. "Review on deep learning based techniques for person re-identification." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (2022): 208–23. http://dx.doi.org/10.17993/3ctic.2022.112.208-223.

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In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person

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Ferreira, Sara, Mário Antunes, and Manuel E. Correia. "A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing." Data 6, no. 8 (2021): 87. http://dx.doi.org/10.3390/data6080087.

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Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital f

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Meshchaninov, Viacheslav Pavlovich, Ivan Andreevich Molodetskikh, Dmitriy Sergeevich Vatolin, and Alexey Gennadievich Voloboy. "Combining contrastive and supervised learning for video super-resolution detection." Keldysh Institute Preprints, no. 80 (2022): 1–13. http://dx.doi.org/10.20948/prepr-2022-80.

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Upscaled video detection is a helpful tool in multimedia forensics, but it’s a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning based super-resolution, and they leave unique traces. This paper proposes a new upscaled-resolution-detection method based on learning of visual representations using contrastive and cross-entropy losses. To explain how the method detects videos, the major components of our framework are systematically reviewed — in particular, it is shown that most dat

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Jiang, Jianguo, Boquan Li, Baole Wei, et al. "FakeFilter: A cross-distribution Deepfake detection system with domain adaptation." Journal of Computer Security 29, no. 4 (2021): 403–21. http://dx.doi.org/10.3233/jcs-200124.

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Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning technologies, also known as Deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. To address those threats imposed by Deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. Differently, some

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Teses / dissertações sobre o assunto "Deep learning for Multimedia Forensics"

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Nowroozi, Ehsan. "Machine Learning Techniques for Image Forensics in Adversarial Setting." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096177.

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The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multi

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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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Budnik, Mateusz. "Active and deep learning for multimedia." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM011.

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Les thèmes principaux abordés dans cette thèse sont l'utilisation de méthodes d'apprentissage actif et d'apprentissage profond dans le contexte du traitement de documents multimodaux. Les contributions proposées dans cette thèse abordent ces deux thèmes. Un système d'apprentissage actif a été introduit pour permettre une annotation plus efficace des émissions de télévision grâce à la propagation des étiquettes, à l'utilisation de données multimodales et à des stratégies de sélection efficaces. Plusieurs scénarios et expériences ont été envisagés dans le cadre de l'identification des personnes

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Ha, Hsin-Yu. "Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2268.

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The rapid advances in technologies make the explosive growth of multimedia data possible and available to the public. Multimedia data can be defined as data collection, which is composed of various data types and different representations. Due to the fact that multimedia data carries knowledgeable information, it has been widely adopted to different genera, like surveillance event detection, medical abnormality detection, and many others. To fulfil various requirements for different applications, it is important to effectively classify multimedia data into semantic concepts across multiple dom

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Vukotic, Verdran. "Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data." Thesis, Rennes, INSA, 2017. http://www.theses.fr/2017ISAR0015/document.

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La thèse porte sur le développement d'architectures neuronales profondes permettant d'analyser des contenus textuels ou visuels, ou la combinaison des deux. De manière générale, le travail tire parti de la capacité des réseaux de neurones à apprendre des représentations abstraites. Les principales contributions de la thèse sont les suivantes: 1) Réseaux récurrents pour la compréhension de la parole: différentes architectures de réseaux sont comparées pour cette tâche sur leurs facultés à modéliser les observations ainsi que les dépendances sur les étiquettes à prédire. 2) Prédiction d’image et

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Hamm, Simon, and sinonh@angliss edu au. "Digital Audio Video Assessment: Surface or Deep Learning - An Investigation." RMIT University. Education, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091216.154300.

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This research aims to investigate an assertion, endorsed by a range of commentators, that multimedia teaching and learning approaches encourage learners to adopt a richer, creative and deeper level of understanding and participation within the learning environment than traditional teaching and learning methods. The thesis examines this assertion by investigating one type of multimedia activity defined (for the purposes of this research) as a digital audio video assessment (DAVA). Data was collected using a constructivist epistemology, interpretative and naturalistic perspective using prim

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Quan, Weize. "Detection of computer-generated images via deep learning." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT076.

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Avec les progrès des outils logiciels d'édition et de génération d'images, il est devenu plus facile de falsifier le contenu des images ou de créer de nouvelles images, même pour les novices. Ces images générées, telles que l'image de rendu photoréaliste et l'image colorisée, ont un réalisme visuel de haute qualité et peuvent potentiellement menacer de nombreuses applications importantes. Par exemple, les services judiciaires doivent vérifier que les images ne sont pas produites par la technologie de rendu infographique, les images colorisées peuvent amener les systèmes de reconnaissance / sur

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MIGLIORELLI, LUCIA. "Towards digital patient monitoring: deep learning methods for the analysis of multimedia data from the actual clinical practice." Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295052.

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Acquisire informazioni sullo stato di salute dei pazienti a partire dall’analisi di video registrazioni è un’opportunità cruciale per potenziare le attuali pratiche cliniche di valutazione e monitoraggio. Questa Tesi di Dottorato propone quattro sistemi automatici che analizzano dati multimediali tramite algoritmi di apprendimento profondo (deep learning). Tali sistemi sono stati sviluppati per arricchire le modalità valutative -ad oggi basate sull’osservazione diretta del paziente da parte del clinico e sulla compilazione di scale cliniche spesso raccolte in formato cartaceo- di tre categorie

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Dutt, Anuvabh. "Continual learning for image classification." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM063.

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Cette thèse traite de l'apprentissage en profondeur appliqu'e aux tâches de classification des images. La principale motivation du travail est de rendre les techniques d’apprentissage en profondeur actuelles plus efficaces et de faire face aux changements dans la distribution des données. Nous travaillons dans le cadre élargi de l’apprentissage continu, dans le but d’avoir 'a l’avenir des modèles d’apprentissage automatique pouvant être améliorés en permanence.Nous examinons d’abord la modification de l’espace étiquette d’un ensemble de données, les échantillons de données restant les mêmes. N

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Darmet, Ludovic. "Vers une approche basée modèle-image flexible et adaptative en criminalistique des images." Thesis, Université Grenoble Alpes, 2020. https://tel.archives-ouvertes.fr/tel-03086427.

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Les images numériques sont devenues un moyen de communication standard et universel. Elles prennent place dans notre vie de tous les jours, ce qui entraîne directement des inquiétudes quant à leur intégrité. Nos travaux de recherche étudient différentes méthodes pour examiner l’authenticité d’une image numérique. Nous nous plaçons dans un contexte réaliste où les images sont en grandes quantités et avec une large diversité de manipulations et falsifications ainsi que de sources. Cela nous a poussé à développer des méthodes flexibles et adaptative face à cette diversité.Nous nous sommes en prem

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Livros sobre o assunto "Deep learning for Multimedia Forensics"

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Anagnostopoulos, Aris, Irene Amerini, Luca Maiano, and Lorenzo Ricciardi Celsi. Deep Learning for Multimedia Forensics. Now Publishers, 2021.

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Arumugam, Chamundeswari, Suresh Jaganathan, Saraswathi S, and Sanjay Misra. Confluence of AI, Machine, and Deep Learning in Cyber Forensics. IGI Global, 2020.

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Arumugam, Chamundeswari, Suresh Jaganathan, Saraswathi S, and Sanjay Misra. Confluence of AI, Machine, and Deep Learning in Cyber Forensics. IGI Global, 2020.

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Arumugam, Chamundeswari, Suresh Jaganathan, Saraswathi S, and Sanjay Misra. Confluence of AI, Machine, and Deep Learning in Cyber Forensics. IGI Global, 2020.

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Arumugam, Chamundeswari, Suresh Jaganathan, Saraswathi S, and Sanjay Misra. Confluence of AI, Machine, and Deep Learning in Cyber Forensics. IGI Global, 2020.

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Arumugam, Chamundeswari, Suresh Jaganathan, Saraswathi S, and Sanjay Misra. Confluence of Ai, Machine, and Deep Learning in Cyber Forensics. Information Science Reference, 2020.

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Capítulos de livros sobre o assunto "Deep learning for Multimedia Forensics"

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Stamm, Matthew C., and Xinwei Zhao. "Anti-Forensic Attacks Using Generative Adversarial Networks." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_17.

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AbstractThe rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat posed by GAN-based anti-forensic attacks. GANs are a powerful machine learning framework that can be used to create realistic, but completely synthetic data. Researchers have recently shown that anti-forensic attacks can be built by using GANs to create synthetic forensic traces. While only a small number of GAN-based anti-forensic attacks currently exist, results show these early attacks are both effective at fooling forensic algorithms and introduce very little distortion into attacked images.

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Long, Chengjiang, Arslan Basharat, and Anthony Hoogs. "Video Frame Deletion and Duplication." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_13.

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AbstractVideos can be manipulated in a number of different ways, including object addition or removal, deep fake videos, temporal removal or duplication of parts of the video, etc. In this chapter, we provide an overview of the previous work related to video frame deletion and duplication and dive into the details of two deep-learning-based approaches for detecting and localizing frame deletion (Chengjiang et al. 2017) and duplication (Chengjiang et al. 2019) manipulations.

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Zampoglou, Markos, Foteini Markatopoulou, Gregoire Mercier, et al. "Detecting Tampered Videos with Multimedia Forensics and Deep Learning." In MultiMedia Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05710-7_31.

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Neves, João C., Ruben Tolosana, Ruben Vera-Rodriguez, Vasco Lopes, Hugo Proença, and Julian Fierrez. "GAN Fingerprints in Face Image Synthesis." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_8.

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AbstractThe availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. This chapter is focused on the analysis of GAN fingerprints in face image synthesis.

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Cozzolino, Davide, and Luisa Verdoliva. "Multimedia Forensics Before the Deep Learning Era." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_3.

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AbstractImage manipulation is as old as photography itself, and powerful media editing tools have been around for a long time. Using such conventional signal processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we will focus on manipulations like adding, replicating, or removing objects and present the major lines of research in multimedia forensics before the deep learning era and the rise of deepfakes. The most popular approaches look for artifacts related to the in-camera processing chain (camera-based clues) or the out-camera processing history (editing-based clues). We will focus on methods that rely on the extraction of a camera fingerprint and need some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review the most interesting features of machine learning- based methods and finally present the major challenges in this area.

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Lyu, Siwei. "DeepFake Detection." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_12.

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AbstractOne particular disconcerting form of disinformation are the impersonating audios/videos backed by advanced AI technologies, in particular, deep neural networks (DNNs). These media forgeries are commonly known as the DeepFakes. The AI-based tools are making it easier and faster than ever to create compelling fakes that are challenging to spot. While there are interesting and creative applications of this technology, it can be weaponized to cause negative consequences. In this chapter, we survey the state-of-the-art DeepFake detection methods.

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Li, Zhuopeng, and Xiaoyan Zhang. "Deep Reinforcement Learning for Automatic Thumbnail Generation." In MultiMedia Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_4.

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Rossetto, Luca, Mahnaz Amiri Parian, Ralph Gasser, Ivan Giangreco, Silvan Heller, and Heiko Schuldt. "Deep Learning-Based Concept Detection in vitrivr." In MultiMedia Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_55.

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Dey, Nilanjan, Amira S. Ashour, and Gia Nhu Nguyen. "Deep Learning for Multimedia Content Analysis." In Mining Multimedia Documents. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21638-14.

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Dey, Nilanjan, Amira S. Ashour, and Gia Nhu Nguyen. "Deep Learning for Multimedia Content Analysis." In Mining Multimedia Documents. CRC Press, 2017. http://dx.doi.org/10.1201/9781315399744-15.

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Trabalhos de conferências sobre o assunto "Deep learning for Multimedia Forensics"

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Verdoliva, Luisa. "Deep Learning in Multimedia Forensics." In IH&MMSec '18: 6th ACM Workshop on Information Hiding and Multimedia Security. ACM, 2018. http://dx.doi.org/10.1145/3206004.3206024.

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Liu, Qingzhong, and Naciye Celebi. "Large Feature Mining and Deep Learning in Multimedia Forensics." In CODASPY '21: Eleventh ACM Conference on Data and Application Security and Privacy. ACM, 2021. http://dx.doi.org/10.1145/3445970.3456285.

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Mayer, Owen, Belhassen Bayar, and Matthew C. Stamm. "Learning Unified Deep-Features for Multiple Forensic Tasks." In IH&MMSec '18: 6th ACM Workshop on Information Hiding and Multimedia Security. ACM, 2018. http://dx.doi.org/10.1145/3206004.3206022.

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Wei, Baole, Min Yu, Kai Chen, and Jianguo Jiang. "Deep-BIF: Blind Image Forensics Based on Deep Learning." In 2019 IEEE Conference on Dependable and Secure Computing (DSC). IEEE, 2019. http://dx.doi.org/10.1109/dsc47296.2019.8937712.

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Nazar, Nidhin, Vinod Kumar Shukla, Gagandeep Kaur, and Nitin Pandey. "Integrating Web Server Log Forensics through Deep Learning." In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2021. http://dx.doi.org/10.1109/icrito51393.2021.9596324.

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Andersson, Maria. "Deep learning for behaviour recognition in surveillance applications." In Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III, edited by Henri Bouma, Robert J. Stokes, Yitzhak Yitzhaky, and Radhakrishna Prabhu. SPIE, 2019. http://dx.doi.org/10.1117/12.2533764.

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Wang, Hsin-Tzu, and Po-Chyi Su. "Deep-Learning-Based Block Similarity Evaluation for Image Forensics." In 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2020. http://dx.doi.org/10.1109/icce-taiwan49838.2020.9258247.

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Buccoli, Michele, Paolo Bestagini, Massimiliano Zanoni, Augusto Sarti, and Stefano Tubaro. "Unsupervised feature learning for bootleg detection using deep learning architectures." In 2014 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2014. http://dx.doi.org/10.1109/wifs.2014.7084316.

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Sang, Jitao, Jun Yu, Ramesh Jain, Rainer Lienhart, Peng Cui, and Jiashi Feng. "Deep Learning for Multimedia." In MM '18: ACM Multimedia Conference. ACM, 2018. http://dx.doi.org/10.1145/3240508.3243931.

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Chien, Jen-Tzung. "Deep Bayesian Multimedia Learning." In MM '20: The 28th ACM International Conference on Multimedia. ACM, 2020. http://dx.doi.org/10.1145/3394171.3418545.

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