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Journal articles on the topic 'Fake Detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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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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<strong>Abstract: </strong>   <strong>Objective-</strong> 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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<strong>Abstract: </strong> <strong>Objective-</strong> 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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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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Hande, Rutuja, Sneha Goon, Aaditi Gondhali, and Navin Singhaniya. "A Novel Method of Deepfake Detection." ITM Web of Conferences 44 (2022): 03064. http://dx.doi.org/10.1051/itmconf/20224403064.

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Deep-Fake is a novel artificial media technology that uses the likeness of someone else to replace people in existing photographs and films. Deep Learning, as the name implies, is a type of Artificial Intelligence that is used to create it. It is critical to develop counter attacking approaches for detecting fraudulent data. This research examines the Deep-Fake technology in depth. The Deep-Fake Detection discussed here is based on current datasets, such as the Deep-Fake Detection Challenge (DFDC) and Google’s Deep-Fake Detection dataset (DFD). The creation of a bespoke dataset from high-quali

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Sabah, Hanady. "Detection of Deep Fake in Face Images Using Deep Learning." Wasit Journal of Computer and Mathematics Science 1, no. 4 (2022): 94–111. http://dx.doi.org/10.31185/wjcm.92.

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Fake images are one of the most widespread phenomena that have a significant influence on our social life, particularly in the world of politics and celeb. Nowadays, generating fake images has become very easy due to the powerful yet simple applications in mobile devices that navigate in the social media world and with the emergence of the Generative Adversarial Network (GAN) that produces images which are indistinguishable to the human eye. Which makes fake images and fake videos easy to perform, difficult to detect, and fast to spread. As a result, image processing and artificial intelligenc

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Journal, IJSREM. "Deep Fake Face Detection Using Deep Learning Tech with LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28624.

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The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in dee

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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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Tyshchenko, Vitalii. "ANALYSIS OF TRAINING METHODS AND NEURAL NETWORK TOOLS FOR FAKE NEWS DETECTION." Cybersecurity: Education, Science, Technique 4, no. 20 (2023): 20–34. http://dx.doi.org/10.28925/2663-4023.2023.20.2034.

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This article analyses various training methods and neural network tools for fake news detection. Approaches to fake news detection based on textual, visual and mixed data are considered, as well as the use of different types of neural networks, such as recurrent neural networks, convolutional neural networks, deep neural networks, generative adversarial networks and others. Also considered are supervised and unsupervised learning methods such as autoencoding neural networks and deep variational autoencoding neural networks. Based on the analysed studies, attention is drawn to the problems asso

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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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Dmello, Swijel, Ridhi Bauskar, and Apoorva Shet. "Fake Reviews Detection Using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 1614–19. http://dx.doi.org/10.21275/mr22507102354.

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Dhanuka, Om, and Dr Shikha Tiwari. "Fake News Detection Using Machine Learning." International Journal of Research Publication and Reviews 5, no. 1 (2024): 2657–64. http://dx.doi.org/10.55248/gengpi.5.0124.0264.

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Mahendrian, Dr N., and Karthick Raja VS. "Fake Product Detection Using QR Code." International Journal of Research Publication and Reviews 5, no. 4 (2024): 1093–96. http://dx.doi.org/10.55248/gengpi.5.0424.0931.

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Arunkumar, P. M., Yalamanchili Sangeetha, P. Vishnu Raja, and S. N. Sangeetha. "Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph." Information Technology and Control 51, no. 3 (2022): 563–74. http://dx.doi.org/10.5755/j01.itc.51.3.31510.

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In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deep fake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deep fake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. This may threaten the public, theref

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Aneena, Babu. "Fake Indian Currency Detection." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 21–25. https://doi.org/10.54105/ijdm.A1640.04010524.

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<strong>Abstract: </strong>The proliferation of counterfeit currency poses a significant threat to both individuals and the national economy. While existing fake currency detection tools are primarily accessible to banks and large enterprises, everyday people and small businesses remain susceptible. Thus, this project aims to delve into the security features of Indian currency and develop a software solution leveraging advanced image processing and computer vision techniques to detect and neutralize counterfeit notes. Counterfeiting currency poses a genuine menace to both the populace's well-b

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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew

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Shukla, Dheeraj. "Deep Fake Face Detection Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50976.

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Artificial Intelligence, deepfake technology, Generative Adversarial Networks GAN, Detection System, Detection Accuracy, User accessibility, Digital content verification. Abstract: In recent years, the rise of deepfake technology has raised significant concerns. regarding the authenticity of digital content. Deepfakes, which are synthetic media created using advanced artificial intelligence techniques, can mislead viewers and pose risks to personal privacy, public trust, and social discourse. The proposed system focuses on developing a Generative Adversarial Network (GAN)- based deepfake detec

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Berrahal, Mohammed, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari, and Idriss Idrissi. "Investigating the effectiveness of deep learning approaches for deep fake detection." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3853–60. http://dx.doi.org/10.11591/eei.v12i6.6221.

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As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This pape

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Yang, Jae-Jun, Seong-Won Cho, and Sun-Tae Chung. "Fake Face Detection System Using Pupil Reflection." Journal of Korean Institute of Intelligent Systems 20, no. 5 (2010): 645–51. http://dx.doi.org/10.5391/jkiis.2010.20.5.645.

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Kim, Youngshin, Jaekeun Na, Seongbeak Yoon, and Juneho Yi. "Masked fake face detection using radiance measurements." Journal of the Optical Society of America A 26, no. 4 (2009): 760. http://dx.doi.org/10.1364/josaa.26.000760.

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Tamtama, Gabriel Indra Widi, and I. Kadek Dendy Senapartha. "Fake Face Detection System Using MobileNets Architecture." CESS (Journal of Computer Engineering, System and Science) 8, no. 2 (2023): 329. http://dx.doi.org/10.24114/cess.v8i2.43762.

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Sistem pengenalan wajah merupakan salah satu metode dalam teknik biometric yang menggunakan wajah untuk proses identifikasi atau verifikasi seseorang. Teknologi ini tidak memerlukan kontak fisik seperti verifikasi sidik jari dan diklaim lebih aman karena wajah setiap orang memiliki karakter yang berbeda-beda. Terdapat dua fase utama dalam sistem biometrik wajah, yaitu deteksi wajah palsu Presentation Attack (PA) detektor dan pengenalan wajah (face recognition). Penelitian ini melakukan eksperimen dengan tujuan membangun sebuah model pembelajaran mesin (machine learning) berbasis mobile untuk m

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M B, Ranjan. "Detection of Face Swapped Deep Fake Videos." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47643.

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ABSTRACT The Deepfake Detector is a novel application designed to enhance digital media integrity by identifying manipulated videos. This project centers on the creation and deployment of an advanced system that continuously analyzes uploaded video content to detect deepfakes. The system employs a deep learning model trained on real and fake video data, utilizing facial recognition and temporal analysis techniques. If a video is determined to be manipulated, the system informs the user with a confidence score and visual indicators, mitigating potential risks associated with deceptive media. Th

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Kumar, Aniket, Saurabh Kumar Pal, Kumar Dhruv Roy, and Mr Ragunthar T. "Fake News Detection." International Journal of Scientific & Engineering Research 11, no. 12 (2020): 575–80. http://dx.doi.org/10.14299/ijser.2020.12.09.

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Now-a-days it's exceedingly common in this digital world that someone for his or her benefit try to manipulate a mass with false information. With the massive use of social media by the population which is beneficial for the users most of the time, can also be used as a really good platform to spread a fake news and at worse try to create chaos in society. Fake death news of celebrities, fake news regarding wars and fake news related to politics are the day-to-day life examples.

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M, Srilekha, Vidyalakshmi D B, Pandhyalamadugu S Uma, Preethi B, and Rachitha M V. "FAKE LOGO DETECTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (2022): 192–96. http://dx.doi.org/10.26562/ijirae.2022.v0908.08.

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This project aims to find fake logos by matching and detecting them against the original logo. This is accomplished by dividing the logo picture into rows and columns and assigning an index value to each cell. The values in each cell are referred as pixels which can be compared with logo image to classify whether it’s a fake logo or original logo. To say whether the logo is fake or the original by looking at the relationship between the pixel values of both the original and the logo in training dataset. The original logo is determined by the pixel values of all cells exactly matching the genui

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Shalini, S. "Fake Image Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1140–45. http://dx.doi.org/10.22214/ijraset.2021.35238.

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In this technological generation, social media plays an important role in people’s daily life. Most of them share text, images and videos on social media(Instagram, Facebook, Twitter ,etc.,). Images are one of the common types of media share among users on social media. So, there is a chance for monitoring of images contained in social media. So most of the people can fabricate these images and disseminate them widely in a very short time, which treats the creditability of the news and public confidence in the means of social communication. So here this research has attempted to propose an app

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Agrawal, Anisha. "Fake News Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1652–57. http://dx.doi.org/10.22214/ijraset.2024.63348.

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Abstract: There has been a considerable rise in the spread of fake news for both commercial and political reasons, which has occurred as a direct result of the fast expansion of online social networks. It is possible for members of online social networks to easily get infected by false news that is spread online via the use of language that is deceptive, which may have substantial repercussions for society that is not online. The quick detection and identification of fake news is an essential goal in the process of strengthening the reliability of information that is shared on social networks

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T, Krithika, Lakshitha B K, Monica Dara, Priya P J, and Veena G. "Fake Currency Detection." International Journal of Innovative Research in Information Security 09, no. 02 (2023): 21–27. http://dx.doi.org/10.26562/ijiris.2023.v0902.04.

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Indian is a developing country, Production, and printing of Fake notes of Rs.100, 500 and 1000 were already there but after the demonetization, the counterfeit notes of new Rs.50,200,500,2000 have also come to the light in very short time and which effects the country’s economic growth. From last few years due to technological advancement in color printing, duplicating, and scanning, counterfeiting problems are coming into the picture. In this article, recognition and verification of paper currency with the help of digital image processing techniques is described. The characteristics extractio

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Agarwala, Avishek, Yousuf Mahmud Fahim, and Mr Nitin Jain. "Fake News Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 432–38. http://dx.doi.org/10.22214/ijraset.2024.59773.

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Abstract: Fake News is a Global problem and is constantly increasing. There are too many people that goes through a fake news and constantly making a new and fake perspective over a subject. To develop a sustainable AI solution is the market demand for many news websites and social platform. The most the work in this era are developed on Backdated Neural Network Classifiers. To engage with latest Model over a highly rated dataset like ISOT Dataset can be a sustainable solution for Fake News Detection. BERT (Bidirectional Encoder Representations from Transformers) can dynamically calculate ever

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V S, SHILFA. "FAKE NEWS DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31643.

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In order to counteract the spread of false information, this study explores the use of machine learning techniques, particularly decision tree classifiers and TF-IDF (Term Frequency-Inverse Document Frequency). Using a dataset of tagged news items, the model is trained to identify patterns that differentiate reliable content from unreliable information.A survey's analysis reveals a worrying trend: fake news has been more common from 2017 to 2018, growing gradually before experiencing a noticeable spike in 2019. But applying current models in 2020 has resulted in a slowdown in the spread of fal

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Rajalakshmi B and Nithin Sebastian. "Fake News Detection." Indian Journal of Data Mining 4, no. 1 (2024): 13–16. http://dx.doi.org/10.54105/ijdm.a1638.04010524.

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The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our method

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Shamini, S. Sweetline, G. Nandhini, R. S. Varshini, and T. Dharini. "Fake Currency Detection." E3S Web of Conferences 491 (2024): 02020. http://dx.doi.org/10.1051/e3sconf/202449102020.

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Due to the great technological developments in the field of color printing in the past few years, it is becoming increasingly recognized that counterfeiting is a serious problem. It used to be possible and very simple for anyone to quickly prepare and print counterfeit currency notes using a computer and a laser printer at homes or places of employment. In the past, only printing houses had these facilities. The most crucial issue is now how to accurately distinguish fake currency from real currency using automatic machines. Almost all nations struggle greatly with the issue of counterfeit cur

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Vinya, Shree Nadipelli, Karla Ravali, and Bathula Meghana. "Fake News Detection." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 57–61. https://doi.org/10.5281/zenodo.7542646.

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Everyone nowadays gets their news from a variety of online sources in the age of the internet. The news quickly reached millions of users thanks to the increasing use of social media platforms like Facebook and Twitter. The most prevalent form of unverified and false information is rumors and fake news, both of which should be flagged as soon as possible to avoid severe consequences. Fake news detection down to the smallest detail remains a significant obstacle. The process of identifying bogus messages is being automated. The "blacklist" of unreliable authors and sources is the most

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Lokhande, Shivam. "Fake News Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44754.

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Abstract— In the contemporary digital era, misinformation poses a significant threat to informed public discourse and democratic processes. This research presents Truth Guardian, an AI-powered web application designed to analyze, verify, and categorize news content in real time. The system integrates sentiment analysis, source credibility verification, and trend monitoring using advanced natural language processing (NLP) and machine learning techniques. By offering features like trend analytics, news dashboards, and source validation, Truth Guardian empowers users to critically evaluate the re

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Chaurasiya, Shomesh. "Fake News Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44757.

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ABSTRACT: The spreading of fake news on social media and computerized stages postures a critical danger to societal judgment and educated decision-making. This paper investigates the challenges and strategies in recognizing fake news, centering on the application of machine learning strategies. We audit existing writing to distinguish key characteristics of fake news, counting its deliberateness creation, quick spread, and changing open gathering. By leveraging apparatuses such as Python's scikit-learn and characteristic dialect preparing (NLP) for printed investigation, we create a directed m

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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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Rajalakshmi, B. "Fake News Detection." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 13–16. https://doi.org/10.54105/ijdm.A1638.04010524.

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<strong>Abstract:</strong> The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Rand

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Goyal, Deepanshu. "Truth Guard: AI-Powered Fake News and Deepfake Detection with Contextual Analysis." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5941–44. https://doi.org/10.22214/ijraset.2025.69787.

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The proliferation of misinformation in the digital age, especially in the form of fake news and deep fakes, poses a serious challenge to societal trust in media. This research explores an AI-powered approach for detecting fake news and deep fake content, utilizing machine learning (ML) and deep learning algorithms, as well as contextual analysis. By integrating natural language processing (NLP) and computer vision techniques, the proposed system aims to enhance detection accuracy across text, audio, and video media. The paper outlines the technologies driving fake news and deep fake generation

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Savitha, A. C., Kumar KM Madhu, M. Pallavi, Chincholi Pallavi, H. B. Prethi, and Rachitha. "Experimental Detection of Deep Fake Images Using Face Swap Algorithm." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 56–61. https://doi.org/10.5281/zenodo.15397033.

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Deepfakes enable highly realistic face-swapping in videos using deep learning. To address the threat posed by Deepfakes, the DFDC dataset, the largest face-swapped video dataset to date, was created with over 100,000 clips generated using multiple methods, including Deepfake Autoencoders and GANs. The dataset consists of videos from 3,426 consenting actors. It supports the development of scalable Deepfake detection models and includes a public Kaggle competition to benchmark solutions. The dataset highlights the complexity of Deepfake detection but shows the potential for generalization to rea

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SanilM, Rithvika, S. Saathvik, Rithesh RaiK, and Srinivas P M. "DEEPFAKE DETECTION USING EYE-BLINKING PATTERN." International Journal of Engineering Applied Sciences and Technology 7, no. 3 (2022): 229–34. http://dx.doi.org/10.33564/ijeast.2022.v07i03.036.

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Deep learning algorithms have become so potent due to increased computing power that it is now relatively easy to produce human-like synthetic videos, sometimes known as & quot; deep fakes. & quot; It is simple to imagine scenarios in which these realistic face switched deep fakes are used to extort individuals, foment political unrest, and stage fake terrorist attacks. This paper provides a deep learning strategy novel for the efficient separation of fraudulent films produced by AI from actual ones. Automatically spotting replacement and recreation deep fakes is possible with our tech

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Wagh, Kavita, Mayank Hindka, Telagamalla Gopi, and Syed Arfath Ahmed. "ENSEMBLE MACHINE LEARNING METHOD FOR DETECTING DEEP FAKES IN SOCIAL PLATFORM." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3216–21. http://dx.doi.org/10.21917/ijivp.2024.0458.

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With the rise of deep fake technology, the detection of manipulated media has become crucial in maintaining the integrity of social platforms. In this study, we propose an ensemble machine learning approach combining Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Decision Trees (DT) for deep fake detection. Our contribution lies in the development of a robust ensemble method that leverages the strengths of multiple algorithms to enhance detection accuracy and resilience against evolving deep fake techniques. Through experimentation on a diverse

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Karnyoto, Andrea, Chengjie Sun, Bingquan Liu, and Xiaolong Wang. "Transfer learning and GRU-CRF augmentation for COVID-19 fake news detection." Computer Science and Information Systems, no. 00 (2021): 53. http://dx.doi.org/10.2298/csis210501053k.

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The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset

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Wang, Bo, Zhao Zhang, Suiyi Zhao, Xianming Ye, Haijun Zhang, and Meng Wang. "FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7518–26. https://doi.org/10.1609/aaai.v39i7.32809.

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Existing face forgery detection methods achieve promising performance when training and testing forgery data are from identical manipulation types, while they fail to generalize well to unseen samples. In this paper, we experimentally investigate and find that the poor generalization of the methods mainly arises from their overfitting on the known fake patterns. Excessively focused on seen fakes, those detectors fail to effectively learn image-intrinsic information and the distributional disparity between real and fake images. Then, to address this issue, we redefine fake learning as real-fake

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Eslam, Fayez, Elsayed Aboutabl Amal, and N. Abdulkader Sarah. "Automated detection of fake news." International Journal of Informatics and Communication Technology 12, no. 1 (2023): 79–84. https://doi.org/10.5281/zenodo.8133600.

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During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99

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Chu, Samuel Kai Wah, Runbin Xie, and Yanshu Wang. "Cross-Language Fake News Detection." Data and Information Management 5, no. 1 (2020): 100–109. http://dx.doi.org/10.2478/dim-2020-0025.

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AbstractWith increasing globalization, news from different countries, and even in different languages, has become readily available and has become a way for many people to learn about other cultures. As people around the world become more reliant on social media, the impact of fake news on public society also increases. However, most of the fake news detection research focuses only on English. In this work, we compared the difference between textual features of different languages (Chinese and English) and their effect on detecting fake news. We also explored the cross-language transmissibilit

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