Bibliografías temáticas / Deep-Fake detection

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Literatura académica sobre el tema "Deep-Fake detection"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Deep-Fake detection"

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Doke, Yash. "Deep fake Detection Through Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 861–66. http://dx.doi.org/10.22214/ijraset.2023.51630.

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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great
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Baveja, Daksh, Yatharth Sharma, and Dr Nagadevi S. "Deep Fake Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36626.

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Abstract—The following paper considers an in-depth study of face detection and classification using a pre-trained VGG16 model with a prime focus on separating real from fake facial images. Face detection is a very fundamental task in computer vision and of key importance in various security- and biometric identification-related applications, social media, and so on, in which the above-mentioned Dortania et al. findings will find their use. The idea is to use transfer learning by tuning an already trained VGG16 that was developed for large-scale image classification to do well in a specific tas
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Shimpi, A. N. "Deep Fake Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47392.

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ABSTRACT The rapid advancement of generative adversarial networks (GANs) and other AI-driven synthesis techniques, deepfake videos have emerged as a significant threat to digital media integrity, enabling the creation of highly realistic but fake video content. These manipulated videos can be used maliciously in disinformation campaigns, identity theft, and other cybercrimes, making their detection a critical challenge. This paper presents a deep learning-based approach for deepfake video detection that leverages both spatial artifacts and temporal inconsistencies introduced during the manipul
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SANJAY M, Mr. "Deep Fake Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47431.

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Abstract - Authenticity of Smart Media A method called Deep Fake identification With Machine Learning uses deep learning approaches to enhance the identification of AI-manipulated media. Artificial intelligence (AI) produces incredibly lifelike synthetic movies known as "deep fakes," which can cause political instability, disinformation, and harm to one's reputation. This project uses preprocessing methods like face cropping and frame extraction to analyse video material. While LSTM is used for temporal sequence modelling to categorise movies as real or deepfake, ResNeXt CNN is employed for fe
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K, Mr Gopi. "Deep Fake Detection using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33196.

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Deep learning is an effective method that is broadly used across a wide range of areas, i.e., computer vision, machine vision, and natural language processing. Deepfakes is an application of this technology where the images and videos of someone are manipulated in such a way that it is difficult for human beings to tell the difference between them and their true selves. Deepfakes have been the subject of several studies recently, and a number of deep learning approaches have been proposed for their detection. Here, we provide an extensive survey on deepfake generation and recognition technique
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D P, Gurukiran. "Deep Fake Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31014.

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Deep learning methods are used by the Deep Fake Detection System to recognize "deepfakes," or distorted media content. Deepfakes are artificial media produced by sophisticated artificial intelligence algorithms that threaten the credibility of media. The goal of our project is to create a reliable system that can discriminate between authentic and modified content in order to stop the spread of false information and protect media integrity. Our goal is to improve deepfake detection efficiency and accuracy by conducting a thorough evaluation of deep learning-based detection techniques. Our tech
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Harsh Vardhan, Naman Varshney, Manoj Kiran R, Pradeep R, and Dr. Latha N.R. "Deep Fake Video Detection." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 830–35. http://dx.doi.org/10.47392/irjaeh.2024.0117.

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Deep fake technology, driven by advancements in artificial intelligence, has garnered significant attention in recent years. This paper synthesizes findings from research papers on deep fake technology, focusing on its misuse and the need for further development. The abstracts of selected papers are analyzed to identify trends, methodologies, and challenges in the field. Common themes include the generation, detection, and mitigation of deep fakes, as well as their societal and ethical implications. Through interdisciplinary collaboration, researchers strive to address the risks associated wit
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Nagashree, K. T., Shristi, Firdaushi Sania, B. Patil Shweta, and Singh Shristi. "Deep-Fake Detection Using Deep Learning." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1700–1706. https://doi.org/10.5281/zenodo.14808073.

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Deep-Fake Detection is a new technology which has caught extreme fashionability in the present generation. Deep-Fake has now held serious pitfalls over spreading misinformation to the world, destroying political faces and also blackmailing individualities to prize centrals. As this technology has taken over the internet in a veritably short span of time and also numerous readily apps are also available to execute Deep-Fake contents, and numerous of the individualities has made systems grounded on detecting the deepfake contents whether it’s fake or real. From the DL(deep learning) &ndash
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Prof. Dikshendra Sarpate, Abrar Mungi, Shreyash Borkar, Shravani Mane, and Kawnain Shaikh. "A Deep Approach to Deep Fake Detection." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 530–34. http://dx.doi.org/10.32628/ijsrset2411274.

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In recent months, the proliferation of free deep learning-based software tools has facilitated the creation of credible face exchanges in videos, resulting in what are known as "DeepFake" (DF) videos. While manipulations of digital videos have been demonstrated for several decades through the use of visual effects, recent advances in deep learning have significantly increased the realism of fake content and the accessibility with which it can be created. These AI-synthesized media, popularly referred to as DF, pose a significant challenge for detection. Detecting DF is a major challenge due to
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A. Sathiya Priya and T. Manisha. "CNN and RNN using Deepfake detection." International Journal of Science and Research Archive 11, no. 2 (2024): 613–18. http://dx.doi.org/10.30574/ijsra.2024.11.2.0460.

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Deep fake Detection is the task of detecting the fake images that have been generated using deep learning techniques. Deep fakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deep fake detection is to identify such manipulations and distinguish them from real videos or images. Deep fake technology has emerged as a significant concern in recent years, presenting challenges in various fields, including media authenticity, privacy, and security.
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Tesis sobre el tema "Deep-Fake detection"

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Zarei, Koosha. "Fake identity & fake activity detection in online social networks based on transfer learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS008.

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Les médias sociaux ont permis de connecter un plus grand nombre de personnes dans le monde entier et d'accroître la facilité d'accès à des contenus gratuits, mais ils sont confrontés à des phénomènes critiques tels que les faux contenus, les fausses identités et les fausses activités. La détection de faux contenus sur les médias sociaux est récemment devenue une recherche émergente qui attire une attention considérable. une recherche émergente qui suscite une attention considérable. Dans ce domaine, les fausses identités jouent un rôle important dans la production et la propagation de faux con
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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.

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Les systèmes biométriques vocaux sont utilisés dans diverses applications pour une authentification sécurisée. Toutefois, ces systèmes sont vulnérables aux attaques par usurpation d'identité. Il est donc nécessaire de disposer de techniques de détection plus robustes. Cette thèse propose de nouvelles techniques de détection fiables et efficaces contre les attaques invisibles. La première contribution est un ensemble non linéaire de classificateurs de sous-bandes utilisant chacun un modèle de mélange gaussien. Des résultats compétitifs montrent que les modèles qui apprennent des indices discrim
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Falade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.

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La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stoc
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Mohawesh, RIM. "Machine learning approaches for fake online reviews detection." Thesis, 2022. https://eprints.utas.edu.au/47578/.

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Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. The truthfulness of online reviews is critical for both consumers and vendors. Genuine reviews can lead to satisfied customers and success for quality businesses, whereas fake reviews can mislead innocent clients, influence customers’ choices owing to false descriptions and inaccurate sales. Therefore, there is a need for efficient fake review detection models and tools that can help distinguish between fraudulent and legitimate reviews to protect
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Palanisamy, Sundar Agnideven. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021. http://dx.doi.org/10.7912/C2/65.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The 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 filt
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(11190282), Agnideven Palanisamy Sundar. "Learning-based Attack and Defense on Recommender Systems." Thesis, 2021.

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The 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
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CHIANG, YAN-MENG, and 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.

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碩士<br>東吳大學<br>資訊管理學系<br>106<br>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 s
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Capítulos de libros sobre el tema "Deep-Fake detection"

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Chambial, Shourya, Rishabh Budhia, Tanisha Pandey, B. K. Tripathy, and A. Tripathy. "Deep Fake Generation and Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_45.

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G, Santhosh Kumar. "Deep Learning for Fake News Detection." In Data Science for Fake News. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_4.

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Sharma, Srishti, Mala Saraswat, and Anil Kumar Dubey. "Fake News Detection Using Deep Learning." In Knowledge Graphs and Semantic Web. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2_19.

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Avram, Camelia, George Mesaroş, and Adina Aştilean. "Deep Neural Networks in Fake News Detection." In Innovations in Mechatronics Engineering II. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09385-2_3.

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Masciari, Elio, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperli. "A Deep Learning Approach to Fake News Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59491-6_11.

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Pimple, Kanchan M., Ravindra R. Solanke, Praveen P. Likhitkar, and Sagar Pande. "Fake Video News Detection Using Deep Learning Algorithm." In Proceedings of Third Doctoral Symposium on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_72.

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Sharma, Vaibhav, Divya Pratap Singh, Jatin Rana, Anjali Kapoor, and Anju Mishra. "Deep Learning Model for Indian Fake Currency Detection." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_8.

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Monisha, G. B., and Jyothi S. Nayak. "Detection of Online Fake Review Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1329-5_13.

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Singh, Aadya, Abey Alex George, Pankaj Gupta, and Lakshmi Gadhikar. "ShallowFake-Detection of Fake Videos Using Deep Learning." In Conference Proceedings of ICDLAIR2019. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_19.

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Nalini, C., R. Shanthakumari, R. Pushpamala, K. Rakshitha, and C. Samyuktha. "Multimodal Fake News Detection Using Deep Learning Techniques." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68905-5_8.

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Actas de conferencias sobre el tema "Deep-Fake detection"

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Manisha, A., S. Reshma Sri, and Viyyapu Lokeshwari Vinya. "Deep Fake Detection Using CNN." In 2024 2nd International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). IEEE, 2024. https://doi.org/10.1109/icaitpr63242.2024.10960037.

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Renuga Devi, R., Bhavana A, Kolle Vishnu Priya, Dharshini P, Batta Jahnavi Chowdary, and Hemala R. "Deep Fake Image and Video Detection using Deep Learning." In 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2024. https://doi.org/10.1109/iccccm61016.2024.11039988.

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Chowdary, B. V., Marry Prabhakar, Mavoori Akhil, Komirishetty Pavan, and B. Pavana Teja Reddy. "Deep Fake Detection using Adversarial Ensemble Techniques." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00041.

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Bobulski, Janusz, and Mariusz Kubanek. "Fake Face Detection Using Deep Neural Network." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825810.

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Rahmani, Saiful Islam, Tarun Pal, Vineet Chaudhary, and Ms Bhumika Nirmohi. "Comparative Analysis of Deep-Fake Detection Methods." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986625.

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Monish, Seelam, Nallaparaju Pranav Varma, and D. Usha Nandini. "Unmasking Deep Fake Images with Intelligent Detection." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076218.

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Singh, Rohan, Dilip Kumar Sharma, and Praphula Kumar Jain. "Efficient net-based deep learning: A deep-fake image detection." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10931956.

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Abdelmohsen, Yasmeen, Khaled Wassif, and Nagy Ramadan. "Fake Reviews Detection Using Deep Learning: A Survey." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652819.

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Bhandarkar, Ankita, Prashant Khobragade, Raju Pawar, Prasad Lokulwar, and Pranay Saraf. "Deep Learning Framework for Robust Deep Fake Image Detection: A Review." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882361.

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Elhag, Salma, Rasha Alharbi, Renad Alsulami, and Aseel Ahmed. "Deep Fake Image Generation, Detection Techniques - Overcoming Challenges of Deep Fake, Ethical Implications Regulation: A Risk-Based Approach." In 2025 2nd International Conference on Advanced Innovations in Smart Cities (ICAISC). IEEE, 2025. https://doi.org/10.1109/icaisc64594.2025.10959687.

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Informes sobre el tema "Deep-Fake detection"

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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1812627.

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