Littérature scientifique sur le sujet « Deep-Fake detection »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Deep-Fake detection ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Deep-Fake detection"
Doke, Yash. « Deep fake Detection Through Deep Learning ». International Journal for Research in Applied Science and Engineering Technology 11, no 5 (31 mai 2023) : 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.
Texte intégralK, Mr Gopi. « Deep Fake Detection using Deep Learning ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 05 (6 mai 2024) : 1–5. http://dx.doi.org/10.55041/ijsrem33196.
Texte intégralD P, Gurukiran. « Deep Fake Detection System ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 04 (17 avril 2024) : 1–5. http://dx.doi.org/10.55041/ijsrem31014.
Texte intégralHarsh Vardhan, Naman Varshney, Manoj Kiran R, Pradeep R et Dr. Latha N.R. « Deep Fake Video Detection ». International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no 04 (17 avril 2024) : 830–35. http://dx.doi.org/10.47392/irjaeh.2024.0117.
Texte intégralProf. Dikshendra Sarpate, Abrar Mungi, Shreyash Borkar, Shravani Mane et Kawnain Shaikh. « A Deep Approach to Deep Fake Detection ». International Journal of Scientific Research in Science, Engineering and Technology 11, no 2 (30 avril 2024) : 530–34. http://dx.doi.org/10.32628/ijsrset2411274.
Texte intégralA. Sathiya Priya et T. Manisha. « CNN and RNN using Deepfake detection ». International Journal of Science and Research Archive 11, no 2 (30 mars 2024) : 613–18. http://dx.doi.org/10.30574/ijsra.2024.11.2.0460.
Texte intégralK. D.V.N.Vaishnavi, L. Hima Bindu, M. Sathvika, K. Udaya Lakshmi, M. Harini et N. Ashok. « Deep learning approaches for robust deep fake detection ». World Journal of Advanced Research and Reviews 21, no 3 (30 mars 2023) : 2283–89. http://dx.doi.org/10.30574/wjarr.2024.21.3.0889.
Texte intégralHande, Rutuja, Sneha Goon, Aaditi Gondhali et Navin Singhaniya. « A Novel Method of Deepfake Detection ». ITM Web of Conferences 44 (2022) : 03064. http://dx.doi.org/10.1051/itmconf/20224403064.
Texte intégralArunkumar, P. M., Yalamanchili Sangeetha, P. Vishnu Raja et S. N. Sangeetha. « Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph ». Information Technology and Control 51, no 3 (23 septembre 2022) : 563–74. http://dx.doi.org/10.5755/j01.itc.51.3.31510.
Texte intégralST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán et Trojovský Pavel. « Deep learning model for deep fake face recognition and detection ». PeerJ Computer Science 8 (22 février 2022) : e881. http://dx.doi.org/10.7717/peerj-cs.881.
Texte intégralThèses sur le sujet "Deep-Fake detection"
Tak, Hemlata. « End-to-End Modeling for Speech Spoofing and Deepfake Detection ». Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS104.pdf.
Texte intégralVoice biometric systems are being used in various applications for secure user authentication using automatic speaker verification technology. However, these systems are vulnerable to spoofing attacks, which have become even more challenging with recent advances in artificial intelligence algorithms. There is hence a need for more robust, and efficient detection techniques. This thesis proposes novel detection algorithms which are designed to perform reliably in the face of the highest-quality attacks. The first contribution is a non-linear ensemble of sub-band classifiers each of which uses a Gaussian mixture model. Competitive results show that models which learn sub-band specific discriminative information can substantially outperform models trained on full-band signals. Given that deep neural networks are more powerful and can perform both feature extraction and classification, the second contribution is a RawNet2 model. It is an end-to-end (E2E) model which learns features directly from raw waveform. The third contribution includes the first use of graph neural networks (GNNs) with an attention mechanism to model the complex relationship between spoofing cues present in spectral and temporal domains. We propose an E2E spectro-temporal graph attention network called RawGAT-ST. RawGAT-ST model is further extended to an integrated spectro-temporal graph attention network, named AASIST which exploits the relationship between heterogeneous spectral and temporal graphs. Finally, this thesis proposes a novel data augmentation technique called RawBoost and uses a self-supervised, pre-trained speech model as a front-end to improve generalisation in the wild conditions
Falade, Joannes Chiderlos. « Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité ». Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.
Texte intégralBiometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection
Mohawesh, RIM. « Machine learning approaches for fake online reviews detection ». Thesis, 2022. https://eprints.utas.edu.au/47578/.
Texte intégralPalanisamy, Sundar Agnideven. « Learning-based Attack and Defense on Recommender Systems ». Thesis, 2021. http://dx.doi.org/10.7912/C2/65.
Texte intégralThe internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.
(11190282), Agnideven Palanisamy Sundar. « Learning-based Attack and Defense on Recommender Systems ». Thesis, 2021.
Trouver le texte intégralCHIANG, YAN-MENG, et 江彥孟. « An empirical study on detecting fake reviews using deep learning and machine learning techniques ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7b939e.
Texte intégral東吳大學
資訊管理學系
106
The increasing share of the online businesses in market economy has led to a larger influence and importance of the online reviews. Before making a purchase, users are increasingly inclined to browse online forum that are posted to share post-purchase experiences of products and services. However, there are many fake reviews in the real world, consumers can't identify authentic and fake reviews. Fake online shopping reviews are harmful to consumers who might buy misrepresented products. Therefore, we proposed a framework which could detect fake reviews. In this study, we focused on the data on the web forum called Mobile01 and used text mining to deal with textual data including Bag-of-words, Latent Semantic Analysis and word2vec for word representation. Next, we used machine learning to train the model to detect fake review, including SVM, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Finally, we chose three best performance models to vote and hope that these fake reviews samples can be the reference in the future research.
Chapitres de livres sur le sujet "Deep-Fake detection"
Chambial, Shourya, Rishabh Budhia, Tanisha Pandey, B. K. Tripathy et A. Tripathy. « Deep Fake Generation and Detection ». Dans Lecture Notes in Networks and Systems, 533–43. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_45.
Texte intégralG, Santhosh Kumar. « Deep Learning for Fake News Detection ». Dans Data Science for Fake News, 71–100. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_4.
Texte intégralSharma, Srishti, Mala Saraswat et Anil Kumar Dubey. « Fake News Detection Using Deep Learning ». Dans Knowledge Graphs and Semantic Web, 249–59. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_19.
Texte intégralAvram, Camelia, George Mesaroş et Adina Aştilean. « Deep Neural Networks in Fake News Detection ». Dans Innovations in Mechatronics Engineering II, 24–35. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09385-2_3.
Texte intégralMasciari, Elio, Vincenzo Moscato, Antonio Picariello et Giancarlo Sperli. « A Deep Learning Approach to Fake News Detection ». Dans Lecture Notes in Computer Science, 113–22. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_11.
Texte intégralPimple, Kanchan M., Ravindra R. Solanke, Praveen P. Likhitkar et Sagar Pande. « Fake Video News Detection Using Deep Learning Algorithm ». Dans Proceedings of Third Doctoral Symposium on Computational Intelligence, 851–57. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_72.
Texte intégralSingh, Aadya, Abey Alex George, Pankaj Gupta et Lakshmi Gadhikar. « ShallowFake-Detection of Fake Videos Using Deep Learning ». Dans Conference Proceedings of ICDLAIR2019, 170–78. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_19.
Texte intégralSharma, Vaibhav, Divya Pratap Singh, Jatin Rana, Anjali Kapoor et Anju Mishra. « Deep Learning Model for Indian Fake Currency Detection ». Dans Algorithms for Intelligent Systems, 115–26. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_8.
Texte intégralMonisha, G. B., et Jyothi S. Nayak. « Detection of Online Fake Review Using Deep Learning ». Dans Lecture Notes in Networks and Systems, 161–72. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1329-5_13.
Texte intégralSinghania, Sneha, Nigel Fernandez et Shrisha Rao. « 3HAN : A Deep Neural Network for Fake News Detection ». Dans Neural Information Processing, 572–81. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_59.
Texte intégralActes de conférences sur le sujet "Deep-Fake detection"
Bide, Pramod, Varun, Gaurav Patil, Samveg Shah et Sakshi Patil. « Fakequipo : Deep Fake Detection ». Dans 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022. http://dx.doi.org/10.1109/gcat55367.2022.9972149.
Texte intégralAggarwal, Aakriti, Siddhant Wadhwa, Pallav Gupta, Nishit Anand et Rashmi Kushwah. « IsSwap : Deep Fake Detection ». Dans 2021 7th International Conference on Signal Processing and Communication (ICSC). IEEE, 2021. http://dx.doi.org/10.1109/icsc53193.2021.9673225.
Texte intégralGupta, Diksha, Shruti Mishra, Meenu Gupta et Rakesh Kumar. « Deep Fake detection using deep learning ». Dans INTERNATIONAL CONFERENCE ON INTELLIGENT AND SMART COMPUTATION (ICIASC-2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0198661.
Texte intégralNguyen, Duc Minh, Tien Huu Do, Robert Calderbank et Nikos Deligiannis. « Fake News Detection using Deep ». Dans Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA : Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1141.
Texte intégralKanchana, M., Vel Murugesh Kumar, T. P. Anish. et Pv Gopirajan. « Deep Fake BERT : Efficient Online Fake News Detection System ». Dans 2023 International Conference on Networking and Communications (ICNWC). IEEE, 2023. http://dx.doi.org/10.1109/icnwc57852.2023.10127560.
Texte intégralThennavan, Swetha, Divya Gokul et Avila Jayapalan. « Deep Learning Based Fake Stamp Detection ». Dans 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128600.
Texte intégralKong, Sheng How, Li Mei Tan, Keng Hoon Gan et Nur Hana Samsudin. « Fake News Detection using Deep Learning ». Dans 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2020. http://dx.doi.org/10.1109/iscaie47305.2020.9108841.
Texte intégralSingh, Yuvraj, Chetanya Arora, Nishant Kumar Lakda, Kartik Tyagi et Deeksha Kumari. « Fake News Detection Using Deep Learning ». Dans 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2023. http://dx.doi.org/10.1109/ic3i59117.2023.10397922.
Texte intégralS, Kingsley, Vasanth Kumar A, Santhosh Prabhu P et Parthiban M. « Deep Fake Detection using Advance ConvNets2D ». Dans 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104847.
Texte intégralAdnan, Sarah Riyadh, et Huda Abdulaali Abdulbaqi. « Investigation of deep fake video detection ». Dans THE FOURTH AL-NOOR INTERNATIONAL CONFERENCE FOR SCIENCE AND TECHNOLOGY (4NICST2022). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0202454.
Texte intégralRapports d'organisations sur le sujet "Deep-Fake detection"
Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), août 2021. http://dx.doi.org/10.2172/1812627.
Texte intégral