Готові списки джерел за темами / Fake Detection

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Добірка наукової літератури з теми "Fake Detection"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Fake Detection"

Ann George, Merin, and Shyma Kareem. "Fake Document Detection." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 2086–89. https://doi.org/10.21275/sr25424104321.

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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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Анотація:

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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ST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, et al. "Deep learning model for deep fake face recognition and detection." PeerJ Computer Science 8 (February 22, 2022): e881. http://dx.doi.org/10.7717/peerj-cs.881.

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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate

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Ms., Dilna e. p1, Maneesha Manoj2 Ms., Jiji c. j3 Ms., Jeena c. j. Ms., and Hrudhya k. p5 Ms. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research 4, no. 1 (2017): 40–48. https://doi.org/10.5281/zenodo.10774726.

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Abstract:   Objective- Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose a

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Murali, Gautham, and Rinsa Rees. "Online Fake News Detection." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1919–23. https://doi.org/10.21275/sr25422163025.

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Patil, Priyadarshini, Vipul Deshpande, Vishal Malge, and Abhishek Bevinmanchi. "Fake Face Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 519–22. http://dx.doi.org/10.22214/ijraset.2022.45829.

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Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and tru

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Ms., Dilna e. p1 Ms. Maneesha Manoj2 Ms. Jiji c. j3 Ms. Jeena c. j. 4. Ms. Hrudhya k. p5. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research, ISSN: 2349 –3607 (Online) , ISSN: 2349 –4824 (Print) Vol.4,, Issue 1, Jan-2017, (2017): pp 40–48. https://doi.org/10.5281/zenodo.242479.

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Анотація:

Abstract: Objective- Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient a

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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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Gujjala Raghu and Dr.B.Mahesh. "FAKE LOGO DETECTION." international journal of engineering technology and management sciences 9, no. 2 (2025): 963–69. https://doi.org/10.46647/ijetms.2025.v09i02.123.

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In the age of digitalization, the proliferation of counterfeit logos has become a significant concern for businesses and consumers alike. Counterfeit logos not only deceive consumers but also tarnish the brand's reputation and result in substantial economic losses. This study addresses the challenge of detecting fake logos using advanced machine learning techniques.We propose a robust framework for fake logo detection that leverages convolutional neural networks (CNNs) to differentiate between authentic and counterfeit logos. The framework consists of a preprocessing step where logos are norma

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Gupta, Parth. "Deep Fake Face Detection Using Deep Learning." International Journal of Research in Science and Technology 15, no. 1 (2025): 68–76. https://doi.org/10.37648/ijrst.v15i01.005.

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Анотація:

The phrase ”Seeing is believing” no longer holds true in today’s world, and this shift has profound consequences across numerous sectors. With the rapid advancement of technology, creating deepfakes has become increasingly accessible, even though mobile applications. Detecting deepfakes is a complex task, and it’s becoming harder for the human eye to identify them. However, some researchers are actively seeking solutions. Deepfakes are synthetic media generated using AI algorithms, where the machine learns features from both the target and source images. The result is the overlaying of the tar

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Дисертації з теми "Fake Detection"

Nordberg, Pontus. "Automatic fake news detection." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18512.

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Анотація:

Due to the large increase in the proliferation of "fake news" in recent years, it has become a widely discussed menace in the online world. In conjunction with this popularity, research of ways to limit the spread has also increased. This paper aims to look at the current research of this area in order to see what automatic fake news detection methods exist and are being developed, which can help online users in protecting themselves against fake news. A systematic literature review is conducted in order to answer this question, with different detection methods discussed in the literature bein

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Satya, Prudhvi Ratna Badri. "Fake Likers Detection on Facebook." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4961.

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Анотація:

In online social networking sites, gaining popularity has become important. The more popular a company is, the more profits it can make. A way to measure a company's popularity is to check how many likes it has (e.g., the company's number of likes in Facebook). To instantly and artificially increase the number of likes, some companies and business people began hiring crowd workers (aka fake likers) who send likes to a targeted page and earn money. Unfortunately, little is known about characteristics of the fake likers and how to identify them. To uncover fake likers in online social networks,

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O'Brien, Nicole (Nicole J. ). "Machine learning for detection of fake news." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119727.

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Анотація:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 55-56). Recent political events have lead to an increase in the popularity and spread of fake news. As demonstrated by the widespread effects of the large onset of fake news, humans are inconsistent if not outright poor d

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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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Анотація:

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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Asresu, Yohannes. "Defining fake news for algorithmic deception detection purposes." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-390393.

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RAJ, CHAHAT. "CONVOLUTIONAL NEURAL NETWORKERS FOR MULTIMODALS FAKE NEWS DETECTION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18816.

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Анотація:

An upsurge of false information revolves around the internet. Social media and websites are flooded with unverified news posts. These posts are comprised of text, images, audio, and videos. There is a requirement for a system that detects fake content in multiple data modalities. We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few. We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility ana

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Kurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.

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Анотація:

Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in

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Yadav, Tarun Kumar. "Automatic Detection and Prevention of Fake Key Attacks in Signal." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/9072.

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The Signal protocol provides end-to-end encryption for billions of users in popular instant messaging applications like WhatsApp, Facebook Messenger, and Google Allo. The protocol relies on an app-specific central server to distribute public keys and relay encrypted messages between the users. Signal prevents passive attacks. However, it is vulnerable to some active attacks due to its reliance on a trusted key server. A malicious key server can distribute fake keys to users to perform man-in-the-middle or impersonation attacks. Signal applications support an authentication ceremony to detect t

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Ghanem, Bilal Hisham Hasan. "On the Detection of False Information: From Rumors to Fake News." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/158570.

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[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traído nuevos retos y amenazas a la web. Estas amenazas han llamado la atención de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que están contaminando las plataformas de redes sociales. Un ejemplo de amenaza serían las noticias falsas, en las que los usuarios difunden y comparten información falsa, inexacta o engañosa. La información falsa no se limita a la información verificable, sino que también incluye información que se utiliza con fines nocivos. Además, uno de los

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Clayton, Spencer Paul. "Malingering Detection among Accommodation-Seeking University Students." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2539.

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Universities have increasingly sought to provide accommodative services to students with learning disorders and Attention-Deficit/Hyperactivity Disorder (ADHD) in recent decades thereby creating a need for diagnostic batteries designed to evaluate cognitive abilities relevant to academic performance. Given that accommodative services (extended time on tests, alternate test forms, etc.) provide incentive to distort impairment steps should be taken to estimate the rate at which students distort impairment and to evaluate the accuracy with which symptom distortion is identified. In order to addre

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Книги з теми "Fake Detection"

Khosravy, Mahdi, Isao Echizen, and Noboru Babaguchi, eds. Frontiers in Fake Media Generation and Detection. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1524-6.

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Kumar Soni, Hemant, Sanjiv Sharma, and G. R. Sinha. Text and Social Media Analytics for Fake News and Hate Speech Detection. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003409519.

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Shu, Kai, and Huan Liu. Detecting Fake News on Social Media. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01915-9.

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Zhang, Cha. Boosting-Based Face Detection and Adaptation. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01809-1.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Multi-Modal Face Presentation Attack Detection. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Advances in Face Presentation Attack Detection. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32906-7.

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1965-, Zhang Zhengyou, ed. Boosting-based face detection and adaptation. Morgan & Claypool, 2010.

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Rathgeb, Christian, Ruben Tolosana, Ruben Vera-Rodriguez, and Christoph Busch, eds. Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7.

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Kawulok, Michal, M. Emre Celebi, and Bogdan Smolka, eds. Advances in Face Detection and Facial Image Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25958-1.

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Tice, Warren K. Guides to button prices & detecting fake or repro buttons. W.K. Tice, 2000.

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Частини книг з теми "Fake Detection"

Palacio Marín, Ignacio, and David Arroyo. "Fake News Detection." In 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57805-3_22.

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Raj, Ansuman Ravi, Lakshay Kaushik, Aamir Suhail, and B. Santhosh. "Fake News Detection." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3485-0_65.

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Mainguet, Jean-François. "Fingerprint Fake Detection." In Encyclopedia of Biometrics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_59.

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Abhishek, Satyam Kumar, and Manoj Kumar. "Fake News Detection." In Data Intelligence and Cognitive Informatics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6460-1_14.

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Long, Si Hong, and Mohd Pouzi Bin Hamzah. "Fake News Detection." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4069-5_25.

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Nathani, Juhi, Smit Madhani, Ishika Khokhani, Parag Patel, and S. Shanthi Therese. "Fake Signature Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9526-0_2.

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Singh, Mahendra, Saurabh Jain, Laraib Khan, and Iaswarchandra. "Fake video detection." In Emerging Trends in Computer Science and Its Application. CRC Press, 2025. https://doi.org/10.1201/9781003606635-94.

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Lata and Yogesh Kumar. "Fake News Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3102-5_31.

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Nguyen, Hai Thanh, Dat Tien Nguyen, Thien Thanh Tran, Huu-Hoa Nguyen, and Nguyen Thai-Nghe. "Fake Face Detection with Separable Convolutions." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63929-6_13.

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Park, Kang Ryoung. "Robust Fake Iris Detection." In Articulated Motion and Deformable Objects. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11789239_2.

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Тези доповідей конференцій з теми "Fake Detection"

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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M, Abisheckraj, S. Sripriya M.E., Chittesh S. P, Kamalesh M, and Nadish D. "CRITIQUESHIELD: Fake Review Detection." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894384.

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Bhat, P. Asha, Chaitra M, Sindhu R. Thyli, Anitha N, and Rajeshwari. "Fake Instagram Profile Detection." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816892.

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Honarjoo, Narges, Fatemeh Taher, and Azadeh Mansouri. "Exploring Feature Map Correlations for Effective Fake Face Detection." In 2024 11th International Symposium on Telecommunications (IST). IEEE, 2024. https://doi.org/10.1109/ist64061.2024.10843660.

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Umadevi, M., S. Barghav Krishna, and N. Sai Kumar. "Deep Fake Face Detection using Efficient Convolutional Neural Networks." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00063.

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Madaan, Vijay, and Neha Sharma. "Detection of Real vs. Fake Face Enhanced by MobileNetV2." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915444.

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Kushwaha, Pradeep Kumar, Kunika Pal, Bhanu Prakash Lohani, Amardeep Gupta, and Archana Chaudhary. "Fake Currency Detection Using A.I." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10941141.

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Uyanage, B. C., and G. U. Ganegoda. "Fake News Detection on Twitter." In 2024 9th International Conference on Information Technology Research (ICITR). IEEE, 2024. https://doi.org/10.1109/icitr64794.2024.10857752.

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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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Yelekar, Vansh, Abhinav Shimpi, Rudra Shete, Ajinkya Shelke, Riddhi Mirajkar, and Suruchi Dedgaonkar. "ML-Powered Fake Account Detection." In 2025 International Conference on Next Generation Communication & Information Processing (INCIP). IEEE, 2025. https://doi.org/10.1109/incip64058.2025.11020092.

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Звіти організацій з теми "Fake Detection"

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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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.

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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads.

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Heisele, Bernd, Tomaso poggio, and Massimilinao Pontil. Face Detection in Still Gray Images. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada459705.

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Rowley, Henry A., Shumeet Baluja, and Takeo Kanade. Rotation Invariant Neural Network-Based Face Detection. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada341629.

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Sung, Kah K., and Tomaso Poggio. Example Based Learning for View-Based Human Face Detection. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada295738.

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Scassellati, Brian. Eye Finding via Face Detection for a Foveated, Active Vision System. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada455661.

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Fredrickson, J. K., and R. J. Seidler. Evaluation of terrestrial microcosms for detection, fate, and survival analysis of genetically engineered microorganisms and their recombinant genetic material. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6409872.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmar

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Polakowski, Michał, and Emma Quinn. Responses to irregularly staying migrants in Ireland. ESRI, 2022. http://dx.doi.org/10.26504/rs140.

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Irregularly staying migrants are more likely to face material deprivation, instability and are more vulnerable to exploitation and crime than legal residents (FRA, 2011). Ultimately, they may face deportation to their country of origin. The fear of detection and deportation can lead to underutilisation of public services (Vintila and Lafleur, 2020). The recent introduction of the Regularisation of Long-Term Undocumented Migrants Scheme (discussed below) is a major policy development that should improve the situation of many people living in Ireland. However, it is likely that irregular migrati

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Pokrzywinski, Kaytee, Kaitlin Volk, Taylor Rycroft, Susie Wood, Tim Davis, and Jim Lazorchak. Aligning research and monitoring priorities for benthic cyanobacteria and cyanotoxins : a workshop summary. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41680.

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In 2018, the US Army Engineer Research and Development Center partnered with the US Army Corps of Engineers–Buffalo District, the US Environmental Protection Agency, Bowling Green State University, and the Cawthron Institute to host a workshop focused on benthic and sediment-associated cyanobacteria and cyanotoxins, particularly in the context of harmful algal blooms (HAB). Technical sessions on the ecology of benthic cyanobacteria in lakes and rivers; monitoring of cyanobacteria and cyanotoxins; detection of benthic and sediment-bound cyanotoxins; and the fate, transport, and health risks of

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