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Journal articles on the topic 'Deep-Fake detection'

Author: Grafiati

Published: 25 May 2024

Last updated: 25 July 2025

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1

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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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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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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K. D.V.N.Vaishnavi, L. Hima Bindu, M. Sathvika, K. Udaya Lakshmi, M. Harini, and N. Ashok. "Deep learning approaches for robust deep fake detection." World Journal of Advanced Research and Reviews 21, no. 3 (2023): 2283–89. http://dx.doi.org/10.30574/wjarr.2024.21.3.0889.

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Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As m

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K., D.V.N.Vaishnavi, Hima Bindu L., Sathvika M., Udaya Lakshmi K., Harini M., and Ashok N. "Deep learning approaches for robust deep fake detection." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 2283–89. https://doi.org/10.5281/zenodo.14176242.

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Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As m

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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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Rai, Mr Yogesh. "Deepfake Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 2116–22. http://dx.doi.org/10.22214/ijraset.2024.62007.

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Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to tra

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Saxena, Amit Kumar, and Kirti Jain. "Fake News Detection Using Deep Learning: A Comprehensive Review." International Journal on Advances in Engineering, Technology and Science (IJAETS) 5, no. 1 (2024): 57–63. https://doi.org/10.5281/zenodo.10711229.

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Abstract– Organizations from various domains are working to find effective solutions for detecting online-based fake news, which is a major issue at the moment. It can be difficult to recognise fake information on the internet because it is frequently written to deceive individuals. Deep learning-based algorithms are more accurate at detecting fake news than many other machine learning techniques. Previous reviews focused on data mining and machine learning approaches, with little attention paid to deep learning techniques for detecting fake news. Emerging deep learning-based techniques

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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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S, Dr Chethan L. "Progress In Deep Fake And Tampering : An In-Depth Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40707.

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The rise of deep fake technology has ignited widespread societal apprehensions about potential security risks and the dissemination of false information. Despite extensive research into deepfake detection, effectively discerning low-quality deepfakes and simultaneously identifying variations in their quality remains a significant and formidable challenge. This investigation explores the dynamic field of deep fake detection, focusing specifically on video analysis targeting facial manipulations. The study introduces Celeb-DF, a substantial dataset comprising high- quality deep fake videos of ce

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Jayshree Kathiriya and Dr. Sheshang Degadwala. "A Review on Fake News Detection using Deep Learning Methods." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 450–60. http://dx.doi.org/10.32628/cseit24103126.

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The proliferation of fake news in online media platforms poses a significant threat to the integrity of information dissemination and public discourse. In response, researchers have increasingly turned to deep learning techniques to develop effective solutions for detecting and mitigating the spread of fake news. This review paper provides a comprehensive overview of recent advances in fake news detection using deep learning methodologies. We survey the literature on various deep learning architectures and approaches employed for fake news detection, including supervised, semi-supervised, and

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Usha MG and Pradeep BM. "Deep fake video/image detection using deep learning." Global Journal of Engineering and Technology Advances 20, no. 2 (2024): 074–80. http://dx.doi.org/10.30574/gjeta.2024.20.2.0148.

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With the widespread of deep fake technology, the potential to detect manipulated images has become an insistent concern. This study investigates the application of machine learning concept and its techniques, precisely CNNs (Convolutional-Neural-Networks) and LSTM (Long-Short-Term-Memory) networks to rectify deep fake images. CNNs are utilized for their strength in feature extraction from images capturing spatial hierarchies in data, while LSTMs are employed to understand the temporal dependencies that might exist in sequential frames of manipulated videos. The proposed theory combines these t

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Usha, MG, and BM Pradeep. "Deep fake video/image detection using deep learning." Global Journal of Engineering and Technology Advances 20, no. 2 (2024): 074–80. https://doi.org/10.5281/zenodo.14921434.

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With the widespread of deep fake technology, the potential to detect manipulated images has become an insistent concern. This study investigates the application of machine learning concept and its techniques, precisely CNNs (Convolutional-Neural-Networks) and LSTM (Long-Short-Term-Memory) networks to rectify deep fake images. CNNs are utilized for their strength in feature extraction from images capturing spatial hierarchies in data, while LSTMs are employed to understand the temporal dependencies that might exist in sequential frames of manipulated videos. The proposed theory combines these t

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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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Dr. Panguluri Vinodh Babu, Musunuri Naga Madhu, Galeeb Shaik, Kornipati Sravani, and Mohammed Nayeemur Rahman. "Fake Image Detection Using Deep Learning." international journal of engineering technology and management sciences 9, no. 2 (2025): 180–89. https://doi.org/10.46647/ijetms.2025.v09i02.025.

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Disinformation and misinformation can be spread through fake images. Fake images can be employed to influence decision-making and manipulate public opinion. Fake image detection finds,use in a number of domains including law enforcement, national security, and social media.It can also be utilized in preventing the diffusion of misinformation and disinformation. The paper recommends a deep learning approach to the detection of forged images based on transfer learning. We utilize a pre-trained CNN weights and adjust them on a set of images used to fake or not, in order to produce a system which

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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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S., Gayathri, Santhiya S., Nowneesh T., Sanjana Shuruthy K., and Sakthi S. "Deep fake detection using deep learning techniques." Applied and Computational Engineering 2, no. 1 (2023): 1010–19. http://dx.doi.org/10.54254/2755-2721/2/20220655.

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Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combinat

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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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Journal, IJSREM. "FAKE REVIEW DETECTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem27893.

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With the exponential growth of online user-generated content, the issue of fake reviews has become a significant concern, impacting consumer decisions and trust in online platforms. Detecting fake reviews manually is challenging due to the sheer volume of reviews generateddaily. This paper proposes a novel approach utilizing deep learning techniques for the automated detection of fake reviews. The study focuses on employing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract meaningful features from textual and contextual information within reviews. The propose

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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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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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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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Pant, Shilpa, and Chhaya Gosavi. "Survey of Deep Fake Creation Technologies and Deep Fake Detection using LSTM." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 377–84. http://dx.doi.org/10.17762/ijritcc.v11i11s.8165.

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Artificial intelligence known as Deep Fake is one of many techniques that have been successfully developed in recent years for altering faces in images and videos. It can produce convincingly faked images, audio, and video. Deep Fake can create problem, especially when there is a media component involved. Even if it is helpful, when it is used maliciously, such as for disseminating fake news or cyber bullying, it can pose a threat to society. It is necessary to develop a complete fake detection method to handle such issues. Too far, numerous methods have been developed to distinguish between a

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Samkeerthana, Pasam. "Deepfake Face Detection Using Machine Learning with LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31975.

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Deep fake videos, which employ artificial intelligence to manipulate and generate highly convincing fake content, have emerged as a significant threat to society, potentially undermining trust in visual media. Detecting these deceptive videos is outmost importance to combat the spread of misinformation and protect the integrity of digital media. In this study, we propose a novel approach for deep fake face video detection utilizing Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN). Our approach capitalizes on the temporal patterns and context within video sequenc

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Borawake, Madhuri. "Deep Fake Audio Recognition Using Deep Learning." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03689.

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Abstract - Deep fake audio is incredibly lifelike synthetic audio that can be produced because to recent advancements in deep learning algorithms. This poses a major threat to digital communications' legitimacy, security, and privacy. Deep fake audio detection has become a critical challenge since current techniques cannot keep up with the rapid advancements in audio synthesis technology. The objective of this study is to develop a dependable deep fake audio detection system using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. The proposed method consistently dist

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Dhage, Sandesh. "Deep-Fake Visual Detection using AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 385–91. https://doi.org/10.22214/ijraset.2025.70081.

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Abstract: In an era where synthetic media is becoming increasingly sophisticated, this project introduces an advanced AIpowered solution designed to detect deepfake content in both images and videos. Deepfakes—media that has been digitally altered or artificially created using machine learning techniques—pose growing threats by facilitating the spread of misinformation, fabricating news content, and infringing on individual privacy. As these manipulated visuals become more convincing and widespread, the need for reliable detection methods becomes more urgent. To address this issue, the system

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Patil, Aditya. "Deepfake Creation & Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44937.

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Deep fake technology has emerged as a potent instrument for creating remarkably authentic synthetic content, encompassing images, videos, and audio recordings. While deep fakes offer promising applications in entertainment and content creation, they also give rise to notable concerns regarding misinformation, privacy violations, and societal manipulation. This paper provides a comprehensive review of deep fake creation techniques, including generative adversarial networks (GANs), autoencoders, and reinforcement learning-based approaches. Furthermore, it examines state-of-the- art methods for d

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Sineglazov, Victor, and Kyrylo Bylym. "Twitter Fake News Detection Using Graph Neural Networks." Electronics and Control Systems 4, no. 78 (2023): 26–33. http://dx.doi.org/10.18372/1990-5548.78.18259.

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This article is devoted to the intellectual processing of text information for the purpose of detecting rail news. To solve the given task, the use of deep graph neural networks is proposed. Fake news detection based on user preferences is augmented with deeper graph neural network topologies, including Hierarchical Graph Pooling with Structure Learning, to improve the graph convolution operation and capture richer contextual relationships in news graphs. The paper presents the possibilities of extending the framework of fake news detection based on user preferences using deep graph neural net

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Sindhu, Medarametla Durga. "Fake Currency Detection Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31943.

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Fake currency is the money produced without the approval of the government, creation of it is considered as a great offence. The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges are turning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-houses, yet presently anybody can print a money p

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KARTHIKEYAN, Mr M., and Binaboina Venkatesh. "Fake Social Media Accounts and Their Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44557.

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The rise of social media has led to the proliferation of fake accounts, which are used for malicious activities such as spreading misinformation, phishing, fraud, and impersonation. These accounts can be automated (bots), human-operated, or hybrid, making their detection challenging. Various techniques, including machine learning, deep learning, and rule-based approaches, are employed to identify fake accounts. Detection methods analyze factors like user behavior, profile characteristics, activity patterns, and network connections. This paper explores the impact of fake social media accounts,

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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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Balasubramanian, Saravana Balaji, Jagadeesh Kannan R, Prabu P, Venkatachalam K, and Pavel Trojovský. "Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection." PeerJ Computer Science 8 (July 13, 2022): e1040. http://dx.doi.org/10.7717/peerj-cs.1040.

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In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of t

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Tamilselvan G and Manas Biswal M. "Voice Cloning & Deep Fake Audio Detection Using Deep Learning." International Journal of Advanced Research and Interdisciplinary Scientific Endeavours 2, no. 1 (2025): 415–19. https://doi.org/10.61359/11.2206-2502.

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Voice cloning and fake audio detection are two critical areas in the field of audio processing and artificial intelligence. Voice cloning aims to synthesize speech with the characteristics of a target speaker, enabling applications such as virtual assistants and personalized voice interfaces. On the other hand, fake audio detection involves identifying manipulated or synthetic audio content, particularly in the context of deep fake technology, combat misinformation and preserving authenticity. In this report, we present a comprehensive overview of voice cloning and fake audio detection techniq

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Khan, Nazakat Farooq, and Ankur Gupta. "Fake News Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1353–60. http://dx.doi.org/10.22214/ijraset.2022.46838.

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Abstract: Social media news may be a double-edged sword. There are a number of benefits to utilizing it: It's simple to use, takes little time, and is user-friendly. It's also simple to share socially significant data with others. On the other hand, a number of social networking sites adapt the news based on personal opinions and interests. This sort of misinformation is spread over social media with the intent of causing harm to a person, organization, or institution. Because of the prevalence of fake news, computer tools are needed to detect it. Fake news detection aims to aid users in spott

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Udayashree, Dr S. "Deep Fake Using Audio and Image Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2716–23. https://doi.org/10.22214/ijraset.2025.70706.

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In the AI-driven era, deep fakes, generated through advanced techniques like Generative Adversarial Networks (GANs), present significant threats by creating highly realistic yet fabricated media. While audio deep fakes have received considerable attention, the detection of manipulated images remains underexplored, creating a critical gap in comprehensive deep fake identification. Our proposed system bridges this gap by integrating transfer learning for enhanced detection across both fake audio and manipulated images. For image analysis, we utilize the VGG19 architecture, leveraging its deep co

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Urane, Kimaya, and Arati Deshpande. "Deep Learning Based Fake News Detection." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (2022): 94–99. http://dx.doi.org/10.17762/ijritcc.v10i7.5578.

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Social network connectivity is one of the most important countermeasures in today's world. we must use with caution or risk creating disaster problems and causing social upheaval. To address this problem, items and stories that spread quickly must be tracked for a set period of time. In this proposed method, we attempted to determine whether the news being disseminated around the world is genuine or not. Factors responsible for fake news detection are also discussed. So that disinformation can be controlled and has a direct impact on society's citizens. Analytical and advanced deep learning te

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Pandey, Mansi, Mayank Kumar, Dhananjay Singh, Anshuman Singh, Pavan Kumar Shukla, and Vinod M. Kapse. "Fake News Detection Using Deep Learning." NIET Journal of Engineering and Technology 10, no. 02S (2022): 18–23. http://dx.doi.org/10.62797/njet.vol.10.issue.02s.004.

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Arsha, Anish, Shouckath Kolliyath Shebin, Joshy Sneha, K. Athira, and A. S. Revathy. "DEEP FAKE DETECTION USING MACHINE LEARNING." Research and Reviews: Advancement in Cyber Security 1, no. 3 (2024): 23–29. https://doi.org/10.5281/zenodo.13382382.

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<em>The recent proliferation of free, deep learning-based tools has democratized the creation of high-fidelity “deepfake” videos, where facial exchanges exhibit minimal manipulation artifacts. While advancements in visual effects have facilitated video manipulation for decades, deep learning has ushered in an era of unprecedented realism and accessibility for generating such “AI-synthesized media.” While crafting deepfakes is now relatively straightforward, their detection remains a significant challenge due to the complexities involved in training algorithms to recogni

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M, Suresh, Bollapalli Sivanjali, Yamuna Kondaveeti, Sivanarayana Desireddy, and Srinivas Reddy Buska. "Generalizable Deep Fake Detection using NPR." International Journal for Modern Trends in Science and Technology 11, no. 04 (2025): 106–12. https://doi.org/10.5281/zenodo.15110674.

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Deepfakes and synthetic media have rapidly evolved into a pervasive threat in today’s digital landscape. With the ability to manipulate and fabricate visual content convincingly, deep fakes pose significant risks to individual privacy, public trust, and national security. This project introduces a novel approach to deep fake detection by rethinking the conventional up-sampling operations in convolutional neural networks (CNNs). By leveraging non-photorealistic rendering (NPR) techniques in tandem with a modified ResNet-based architecture, the proposed system is designed to generalize wel

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Gorrela, Samuel Kiran Babu, Venkata Suryanarayana Balakurthi, Sasanka Reddy Kethireddy, and Tamilselvi K. "Deep Fake Images and Videos Detection using Deep Learning." Journal of Information Technology and Digital World 7, no. 2 (2025): 155–73. https://doi.org/10.36548/jitdw.2025.2.006.

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Deepfake technology has now become an actual menace in the digital media world, as it has the ability to generate highly realistic manipulated media. It poses significant questions regarding misinformation, identity impersonation, and cyber fraud against public personalities like politicians, celebrities, and influencers. Deepfakes are mainly produced by Generative Adversarial Networks (GANs), autoencoders, and Convolutional Neural Networks (CNNs). Even though GANs create synthetic visual data using adversarial training and competition between a discriminator and a generator, autoencoders are

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Nidhi, Gajimwar Ashmi Dahiwale Isha Walde Shyamal Dhabarde Prof. Monika Walde. "Automated Image Forgery Detection With Python." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 133–38. https://doi.org/10.5281/zenodo.12516039.

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Fake image detection has become increasingly important due to the widespread use of image editing software and the proliferation of fake images on social media and other online platforms. In this project, we propose a Python-based approach for detecting fake images using deep learning techniques. Our method involves preprocessing the images, extracting relevant features using convolutional neural networks (CNNs), and training a classifier to distinguish between real and fake images. We leverage state-of-the-art deep learning frameworks such as TensorFlow or PyTorch for model development and ev

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