Literatura académica sobre el tema "DETECTING DEEPFAKES"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "DETECTING DEEPFAKES".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "DETECTING DEEPFAKES"
Mai, Kimberly T., Sergi Bray, Toby Davies y Lewis D. Griffin. "Warning: Humans cannot reliably detect speech deepfakes". PLOS ONE 18, n.º 8 (2 de agosto de 2023): e0285333. http://dx.doi.org/10.1371/journal.pone.0285333.
Texto completoDobber, Tom, Nadia Metoui, Damian Trilling, Natali Helberger y Claes de Vreese. "Do (Microtargeted) Deepfakes Have Real Effects on Political Attitudes?" International Journal of Press/Politics 26, n.º 1 (25 de julio de 2020): 69–91. http://dx.doi.org/10.1177/1940161220944364.
Texto completoVinogradova, Ekaterina. "The malicious use of political deepfakes and attempts to neutralize them in Latin America". Latinskaia Amerika, n.º 5 (2023): 35. http://dx.doi.org/10.31857/s0044748x0025404-3.
Texto completoSingh, Preeti, Khyati Chaudhary, Gopal Chaudhary, Manju Khari y Bharat Rawal. "A Machine Learning Approach to Detecting Deepfake Videos: An Investigation of Feature Extraction Techniques". Journal of Cybersecurity and Information Management 9, n.º 2 (2022): 42–50. http://dx.doi.org/10.54216/jcim.090204.
Texto completoDas, Rashmiranjan, Gaurav Negi y Alan F. Smeaton. "Detecting Deepfake Videos Using Euler Video Magnification". Electronic Imaging 2021, n.º 4 (18 de enero de 2021): 272–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-272.
Texto completoRaza, Ali, Kashif Munir y Mubarak Almutairi. "A Novel Deep Learning Approach for Deepfake Image Detection". Applied Sciences 12, n.º 19 (29 de septiembre de 2022): 9820. http://dx.doi.org/10.3390/app12199820.
Texto completoJameel, Wildan J., Suhad M. Kadhem y Ayad R. Abbas. "Detecting Deepfakes with Deep Learning and Gabor Filters". ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, n.º 1 (18 de marzo de 2022): 18–22. http://dx.doi.org/10.14500/aro.10917.
Texto completoGiudice, Oliver, Luca Guarnera y Sebastiano Battiato. "Fighting Deepfakes by Detecting GAN DCT Anomalies". Journal of Imaging 7, n.º 8 (30 de julio de 2021): 128. http://dx.doi.org/10.3390/jimaging7080128.
Texto completoLim, Suk-Young, Dong-Kyu Chae y Sang-Chul Lee. "Detecting Deepfake Voice Using Explainable Deep Learning Techniques". Applied Sciences 12, n.º 8 (13 de abril de 2022): 3926. http://dx.doi.org/10.3390/app12083926.
Texto completoGadgilwar, Jitesh, Kunal Rahangdale, Om Jaiswal, Parag Asare, Pratik Adekar y Prof Leela Bitla. "Exploring Deepfakes - Creation Techniques, Detection Strategies, and Emerging Challenges: A Survey". International Journal for Research in Applied Science and Engineering Technology 11, n.º 3 (31 de marzo de 2023): 1491–95. http://dx.doi.org/10.22214/ijraset.2023.49681.
Texto completoTesis sobre el tema "DETECTING DEEPFAKES"
Hasanaj, Enis, Albert Aveler y William Söder. "Cooperative edge deepfake detection". Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.
Texto completoGardner, Angelica. "Stronger Together? An Ensemble of CNNs for Deepfakes Detection". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97643.
Texto completoEmir, Alkazhami. "Facial Identity Embeddings for Deepfake Detection in Videos". Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170587.
Texto completoGUARNERA, LUCA. "Discovering Fingerprints for Deepfake Detection and Multimedia-Enhanced Forensic Investigations". Doctoral thesis, Università degli studi di Catania, 2021. http://hdl.handle.net/20.500.11769/539620.
Texto completoSONI, ANKIT. "DETECTING DEEPFAKES USING HYBRID CNN-RNN MODEL". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19168.
Texto completoRASOOL, AALE. "DETECTING DEEPFAKES WITH MULTI-MODEL NEURAL NETWORKS: A TRANSFER LEARNING APPROACH". Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19993.
Texto completoChang, Ching-Tang y 張景棠. "Detecting Deepfake Videos with CNN and Image Partitioning". Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394052%22.&searchmode=basic.
Texto completo國立中興大學
資訊科學與工程學系所
107
The AIgenerated images are gradually similar to the pictures taken. When the generated images are used in inappropriate cases, it will cause damage to people’s rights and benefits. These doubtful images will cause illegal problems. The issue of detecting digital forgery has existed for many years. However, the fake images generated by the development of science and technology are more difficult to distinguish. Therefore, this thesis based on deep learning technology to detect the controversial face manipulation images. We proposed to segment the image block by block method and use CNN to train the features of each block separately. Finally, each feature is voted in an ensemble model to detect forgery images. Accurately, we recognize Faceswap, DeepFakes, and Face2Face with the dataset provided by FaceForensics++. Nowadays, classifiers require not only high accuracy but also the robustness of different datasets. Therefore, we train some data to test whether it is robust in other data. We collected digital forgeries generated by different methods on the videosharing platform to test the generalization of our model in detecting these forgeries.
Libros sobre el tema "DETECTING DEEPFAKES"
Gaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.
Buscar texto completoGaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.
Buscar texto completoGaur, Loveleen. Deepfakes: Creation, Detection, and Impact. Taylor & Francis Group, 2022.
Buscar texto completoGaur, Loveleen. Deepfakes: Creation, Detection, and Impact. CRC Press LLC, 2022.
Buscar texto completoGaur, Loveleen. Deepfakes: Creation, Detection, and Impact. CRC Press, 2022.
Buscar texto completoBusch, Christoph, Christian Rathgeb, Ruben Vera-Rodriguez y Ruben Tolosana. Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks. Springer International Publishing AG, 2021.
Buscar texto completoBusch, Christoph, Christian Rathgeb, Ruben Vera-Rodriguez y Ruben Tolosana. Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks. Springer International Publishing AG, 2021.
Buscar texto completoAbdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal y Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.
Buscar texto completoAbdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal y Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.
Buscar texto completoAbdul-Majeed, Ghassan H., Adriana Burlea-Schiopoiu, Parul Aggarwal y Ahmed J. Obaid. Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications. IGI Global, 2022.
Buscar texto completoCapítulos de libros sobre el tema "DETECTING DEEPFAKES"
Korshunov, Pavel y Sébastien Marcel. "The Threat of Deepfakes to Computer and Human Visions". En Handbook of Digital Face Manipulation and Detection, 97–115. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_5.
Texto completoZobaed, Sm, Fazle Rabby, Istiaq Hossain, Ekram Hossain, Sazib Hasan, Asif Karim y Khan Md. Hasib. "DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning". En Advanced Sciences and Technologies for Security Applications, 177–201. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88040-8_7.
Texto completoLi, Yuezun, Pu Sun, Honggang Qi y Siwei Lyu. "Toward the Creation and Obstruction of DeepFakes". En Handbook of Digital Face Manipulation and Detection, 71–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_4.
Texto completoHernandez-Ortega, Javier, Ruben Tolosana, Julian Fierrez y Aythami Morales. "DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame". En Handbook of Digital Face Manipulation and Detection, 255–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_12.
Texto completoLyu, Siwei. "DeepFake Detection". En Multimedia Forensics, 313–31. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_12.
Texto completoHao, Hanxiang, Emily R. Bartusiak, David Güera, Daniel Mas Montserrat, Sriram Baireddy, Ziyue Xiang, Sri Kalyan Yarlagadda et al. "Deepfake Detection Using Multiple Data Modalities". En Handbook of Digital Face Manipulation and Detection, 235–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_11.
Texto completoRaturi, Sonali, Amit Kumar Mishra y Srabanti Maji. "Fake News Detection Using Machine Learning". En DeepFakes, 121–33. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-10.
Texto completoRastogi, Shreya, Amit Kumar Mishra y Loveleen Gaur. "Detection of DeepFakes Using Local Features and Convolutional Neural Network". En DeepFakes, 73–89. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003231493-6.
Texto completoBhilare, Omkar, Rahul Singh, Vedant Paranjape, Sravan Chittupalli, Shraddha Suratkar y Faruk Kazi. "DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAs". En Parallel and Distributed Computing, Applications and Technologies, 45–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29927-8_4.
Texto completoJiang, Liming, Wayne Wu, Chen Qian y Chen Change Loy. "DeepFakes Detection: the Dataset and Challenge". En Handbook of Digital Face Manipulation and Detection, 303–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_14.
Texto completoActas de conferencias sobre el tema "DETECTING DEEPFAKES"
Celebi, Naciye, Qingzhong Liu y Muhammed Karatoprak. "A Survey of Deep Fake Detection for Trial Courts". En 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120919.
Texto completoKumar, Akash, Arnav Bhavsar y Rajesh Verma. "Detecting Deepfakes with Metric Learning". En 2020 8th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2020. http://dx.doi.org/10.1109/iwbf49977.2020.9107962.
Texto completoDheeraj, J. C., Krutant Nandakumar, A. V. Aditya, B. S. Chethan y G. C. R. Kartheek. "Detecting Deepfakes Using Deep Learning". En 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). IEEE, 2021. http://dx.doi.org/10.1109/rteict52294.2021.9573740.
Texto completoLacerda, Gustavo Cunha y Raimundo Claudio da Silva Vasconcelos. "A Machine Learning Approach for DeepFake Detection". En Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sibgrapi.est.2022.23272.
Texto completoShiohara, Kaede y Toshihiko Yamasaki. "Detecting Deepfakes with Self-Blended Images". En 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01816.
Texto completoMallet, Jacob, Rushit Dave, Naeem Seliya y Mounika Vanamala. "Using Deep Learning to Detecting Deepfakes". En 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2022. http://dx.doi.org/10.1109/iscmi56532.2022.10068449.
Texto completoKhichi, Manish y Rajesh Kumar Yadav. "Analyzing the Methods for Detecting Deepfakes". En 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021. http://dx.doi.org/10.1109/icac3n53548.2021.9725773.
Texto completoMalik, Yushaa Shafqat, Nosheen Sabahat y Muhammad Osama Moazzam. "Image Animations on Driving Videos with DeepFakes and Detecting DeepFakes Generated Animations". En 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE, 2020. http://dx.doi.org/10.1109/inmic50486.2020.9318064.
Texto completoHosler, Brian, Davide Salvi, Anthony Murray, Fabio Antonacci, Paolo Bestagini, Stefano Tubaro y Matthew C. Stamm. "Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies". En 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00112.
Texto completoHe, Yang, Ning Yu, Margret Keuper y Mario Fritz. "Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/349.
Texto completo