Literatura científica selecionada sobre o tema "Deep-Fake detection"
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Artigos de revistas sobre o assunto "Deep-Fake detection"
Doke, Yash. "Deep fake Detection Through Deep Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de maio de 2023): 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.
Texto completo da fonteK, Mr Gopi. "Deep Fake Detection using Deep Learning". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 05 (6 de maio de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33196.
Texto completo da fonteD P, Gurukiran. "Deep Fake Detection System". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 04 (17 de abril de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31014.
Texto completo da fonteHarsh Vardhan, Naman Varshney, Manoj Kiran R, Pradeep R e Dr. Latha N.R. "Deep Fake Video Detection". International Research Journal on Advanced Engineering Hub (IRJAEH) 2, n.º 04 (17 de abril de 2024): 830–35. http://dx.doi.org/10.47392/irjaeh.2024.0117.
Texto completo da fonteProf. Dikshendra Sarpate, Abrar Mungi, Shreyash Borkar, Shravani Mane e Kawnain Shaikh. "A Deep Approach to Deep Fake Detection". International Journal of Scientific Research in Science, Engineering and Technology 11, n.º 2 (30 de abril de 2024): 530–34. http://dx.doi.org/10.32628/ijsrset2411274.
Texto completo da fonteA. Sathiya Priya e T. Manisha. "CNN and RNN using Deepfake detection". International Journal of Science and Research Archive 11, n.º 2 (30 de março de 2024): 613–18. http://dx.doi.org/10.30574/ijsra.2024.11.2.0460.
Texto completo da fonteK. D.V.N.Vaishnavi, L. Hima Bindu, M. Sathvika, K. Udaya Lakshmi, M. Harini e N. Ashok. "Deep learning approaches for robust deep fake detection". World Journal of Advanced Research and Reviews 21, n.º 3 (30 de março de 2023): 2283–89. http://dx.doi.org/10.30574/wjarr.2024.21.3.0889.
Texto completo da fonteHande, Rutuja, Sneha Goon, Aaditi Gondhali e Navin Singhaniya. "A Novel Method of Deepfake Detection". ITM Web of Conferences 44 (2022): 03064. http://dx.doi.org/10.1051/itmconf/20224403064.
Texto completo da fonteArunkumar, P. M., Yalamanchili Sangeetha, P. Vishnu Raja e S. N. Sangeetha. "Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph". Information Technology and Control 51, n.º 3 (23 de setembro de 2022): 563–74. http://dx.doi.org/10.5755/j01.itc.51.3.31510.
Texto completo da fonteST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán e Trojovský Pavel. "Deep learning model for deep fake face recognition and detection". PeerJ Computer Science 8 (22 de fevereiro de 2022): e881. http://dx.doi.org/10.7717/peerj-cs.881.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteVoice 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.
Texto completo da fonteBiometrics 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/.
Texto completo da fontePalanisamy, Sundar Agnideven. "Learning-based Attack and Defense on Recommender Systems". Thesis, 2021. http://dx.doi.org/10.7912/C2/65.
Texto completo da fonteThe 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.
Encontre o texto completo da fonteCHIANG, YAN-MENG, e 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.
Texto completo da fonte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.
Capítulos de livros sobre o assunto "Deep-Fake detection"
Chambial, Shourya, Rishabh Budhia, Tanisha Pandey, B. K. Tripathy e A. Tripathy. "Deep Fake Generation and Detection". In 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.
Texto completo da fonteG, Santhosh Kumar. "Deep Learning for Fake News Detection". In Data Science for Fake News, 71–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_4.
Texto completo da fonteSharma, Srishti, Mala Saraswat e Anil Kumar Dubey. "Fake News Detection Using Deep Learning". In Knowledge Graphs and Semantic Web, 249–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_19.
Texto completo da fonteAvram, Camelia, George Mesaroş e Adina Aştilean. "Deep Neural Networks in Fake News Detection". In Innovations in Mechatronics Engineering II, 24–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09385-2_3.
Texto completo da fonteMasciari, Elio, Vincenzo Moscato, Antonio Picariello e Giancarlo Sperli. "A Deep Learning Approach to Fake News Detection". In Lecture Notes in Computer Science, 113–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_11.
Texto completo da fontePimple, Kanchan M., Ravindra R. Solanke, Praveen P. Likhitkar e Sagar Pande. "Fake Video News Detection Using Deep Learning Algorithm". In 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.
Texto completo da fonteSingh, Aadya, Abey Alex George, Pankaj Gupta e Lakshmi Gadhikar. "ShallowFake-Detection of Fake Videos Using Deep Learning". In Conference Proceedings of ICDLAIR2019, 170–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_19.
Texto completo da fonteSharma, Vaibhav, Divya Pratap Singh, Jatin Rana, Anjali Kapoor e Anju Mishra. "Deep Learning Model for Indian Fake Currency Detection". In Algorithms for Intelligent Systems, 115–26. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_8.
Texto completo da fonteMonisha, G. B., e Jyothi S. Nayak. "Detection of Online Fake Review Using Deep Learning". In 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.
Texto completo da fonteSinghania, Sneha, Nigel Fernandez e Shrisha Rao. "3HAN: A Deep Neural Network for Fake News Detection". In Neural Information Processing, 572–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_59.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Deep-Fake detection"
Bide, Pramod, Varun, Gaurav Patil, Samveg Shah e Sakshi Patil. "Fakequipo: Deep Fake Detection". In 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). IEEE, 2022. http://dx.doi.org/10.1109/gcat55367.2022.9972149.
Texto completo da fonteAggarwal, Aakriti, Siddhant Wadhwa, Pallav Gupta, Nishit Anand e Rashmi Kushwah. "IsSwap: Deep Fake Detection". In 2021 7th International Conference on Signal Processing and Communication (ICSC). IEEE, 2021. http://dx.doi.org/10.1109/icsc53193.2021.9673225.
Texto completo da fonteGupta, Diksha, Shruti Mishra, Meenu Gupta e Rakesh Kumar. "Deep Fake detection using deep learning". In INTERNATIONAL CONFERENCE ON INTELLIGENT AND SMART COMPUTATION (ICIASC-2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0198661.
Texto completo da fonteNguyen, Duc Minh, Tien Huu Do, Robert Calderbank e Nikos Deligiannis. "Fake News Detection using Deep". In 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.
Texto completo da fonteKanchana, M., Vel Murugesh Kumar, T. P. Anish. e Pv Gopirajan. "Deep Fake BERT: Efficient Online Fake News Detection System". In 2023 International Conference on Networking and Communications (ICNWC). IEEE, 2023. http://dx.doi.org/10.1109/icnwc57852.2023.10127560.
Texto completo da fonteThennavan, Swetha, Divya Gokul e Avila Jayapalan. "Deep Learning Based Fake Stamp Detection". In 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128600.
Texto completo da fonteKong, Sheng How, Li Mei Tan, Keng Hoon Gan e Nur Hana Samsudin. "Fake News Detection using Deep Learning". In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2020. http://dx.doi.org/10.1109/iscaie47305.2020.9108841.
Texto completo da fonteSingh, Yuvraj, Chetanya Arora, Nishant Kumar Lakda, Kartik Tyagi e Deeksha Kumari. "Fake News Detection Using Deep Learning". In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2023. http://dx.doi.org/10.1109/ic3i59117.2023.10397922.
Texto completo da fonteS, Kingsley, Vasanth Kumar A, Santhosh Prabhu P e Parthiban M. "Deep Fake Detection using Advance ConvNets2D". In 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104847.
Texto completo da fonteAdnan, Sarah Riyadh, e Huda Abdulaali Abdulbaqi. "Investigation of deep fake video detection". In THE FOURTH AL-NOOR INTERNATIONAL CONFERENCE FOR SCIENCE AND TECHNOLOGY (4NICST2022). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0202454.
Texto completo da fonteRelatórios de organizações sobre o assunto "Deep-Fake detection"
Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), agosto de 2021. http://dx.doi.org/10.2172/1812627.
Texto completo da fonte