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Journal articles on the topic 'Deepfake Detection'

Author: Grafiati

Published: 25 January 2025

Last updated: 25 July 2025

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1

Yasrab, Robail, Wanqi Jiang, and Adnan Riaz. "Fighting Deepfakes Using Body Language Analysis." Forecasting 3, no. 2 (2021): 303–21. http://dx.doi.org/10.3390/forecast3020020.

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Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepf

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Athawale, Prof. S. V., Shreyash Vyawahare, Priyanshu Marodkar, Srushti Lanjewar, and Pratiksha Tawar. "Deepfake Detection Model." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 2 (2024): 195–202. https://doi.org/10.5281/zenodo.11180891.

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<em>Deepfakes are a type of synthetic media that can be used to create realistic videos of people saying or doing things they never did. This raises concerns about the potential for deepfakes to be used to spread misinformation or propaganda. In this project, we present a deepfake detection module that can be used to identify deepfakes with high accuracy. The deepfake detection module is based on a pre-trained InceptionResNetV2 model that is fine-tuned on a dataset of real and deepfake videos. The model is able to extract features from the videos that are indicative of whether they are real or

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Niveditha, Zohaib Hasan Princy, Saurabh Sharma, Vishal Paranjape, and Abhishek Singh. "Review of Deep Learning Techniques for Deepfake Image Detection." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 11, no. 02 (2022): 1–14. http://dx.doi.org/10.15662/ijareeie.2022.1102021.

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Deepfake is an advanced synthetic media technology that generates convincingly authentic yet fake images and videos by modifying a person's likeness. The term "Deepfake" is a blend of "Deep learning" and "Fake," highlighting the use of artificial intelligence and deep learning algorithms in its creation. Deepfake generation involves training models to learn the nuances of facial attributes, expressions, motion, and speech patterns to produce fabricated media indistinguishable from real footage. Deepfakes are often used to manipulate human content, especially the invariant facial regions. The s

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Sunkari, Venkateswarlu, and Ayyagari Sri Nagesh. "Artificial intelligence for deepfake detection: systematic review and impact analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3786. http://dx.doi.org/10.11591/ijai.v13.i4.pp3786-3792.

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<p>Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand the

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Venkateswarlu, Sunkari, and Sri Nagesh Ayyagari. "Artificial intelligence for deepfake detection: systematic review and impact analysis." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3786–92. https://doi.org/10.11591/ijai.v13.i4.pp3786-3792.

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Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand them. Despit

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Battula Thirumaleshwari Devi, Et al. "A Comprehensive Survey on Deepfake Methods: Generation, Detection, and Applications." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 654–78. http://dx.doi.org/10.17762/ijritcc.v11i9.8857.

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Due to recent advancements in AI and deep learning, several methods and tools for multimedia transformation, known as deepfake, have emerged. A deepfake is a synthetic media where a person's resemblance is used to substitute their presence in an already-existing image or video. Deepfakes have both positive and negative implications. They can be used in politics to simulate events or speeches, in translation to provide natural-sounding translations, in education for virtual experiences, and in entertainment for realistic special effects. The emergence of deepfake face forgery on the internet ha

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Lad, Sumit. "Adversarial Approaches to Deepfake Detection: A Theoretical Framework for Robust Defense." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 46–58. http://dx.doi.org/10.60087/jaigs.v6i1.225.

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The rapid improvements in capabilities of neural networks and generative adversarial networks (GANs) has given rise to extremely sophisticated deepfake technologies. This has made it very difficult to reliably recognize fake digital content. It has enabled the creation of highly convincing synthetic media which can be used in malicious ways in this era of user generated information and social media. Existing deepfake detection techniques are effective against early iterations of deepfakes but get increasingly vulnerable to more sophisticated deepfakes and adversarial attacks. In this paper we

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S, SARANYA. "Deepfake Detection using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46605.

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Abstract—Deepfake technology, driven by generative adversarial networks (GANs), poses significant challenges in digital security, misinformation, and privacy. Detecting deepfakes in images and videos requires advanced deep learning models. This study explores deepfake detection using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures like Vision Transformers (ViTs). We employ Meso4_DF deepfake detection pipeline that uses TensorFlow/Keras, PyTorch, OpenCV for processing, with Dlib, Scikit-Image, and NumPy for feature extraction, leveragi

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Krueger, Natalie, Mounika Vanamala, and Rushit Dave. "Recent Advancements in the Field of Deepfake Detection." International Journal of Computer Science and Information Technology 15, no. 4 (2023): 01–11. http://dx.doi.org/10.5121/ijcsit.2023.15401.

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A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use b

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S. Praveena, R.Kaviya, K.Sheerin Farhana, and S.Bhuvanasri. "Deep Fake Video Detection Using Transfer Learning Resnet50." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 585–90. https://doi.org/10.47392/irjaem.2025.0094.

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The rapid development of deep learning technologies has enabled the creation of highly realistic deepfake videos, raising concerns in areas such as media integrity, privacy, and security. Detecting these deepfakes has become a significant challenge, as conventional methods struggle to keep pace with increasingly sophisticated techniques. This journal explores the application of transfer learning using ResNet50, a pre-trained convolutional neural network, for deepfake video detection. We present an overview of deepfake creation, the role of ResNet50 in transfer learning, the implementation proc

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Kawabe, Akihisa, Ryuto Haga, Yoichi Tomioka, Jungpil Shin, and Yuichi Okuyama. "A Dynamic Ensemble Selection of Deepfake Detectors Specialized for Individual Face Parts." Electronics 12, no. 18 (2023): 3932. http://dx.doi.org/10.3390/electronics12183932.

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The development of deepfake technology, based on deep learning, has made it easier to create images of fake human faces that are indistinguishable from the real thing. Many deepfake methods and programs are publicly available and can be used maliciously, for example, by creating fake social media accounts with images of non-existent human faces. To prevent the misuse of such fake images, several deepfake detection methods have been proposed as a countermeasure and have proven capable of detecting deepfakes with high accuracy when the target deepfake model has been identified. However, the exis

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Chen, Junyi, Minghao Yang, and Kaishen Yuan. "A Review of Deepfake Detection Techniques." Applied and Computational Engineering 117, no. 1 (2025): 165–74. https://doi.org/10.54254/2755-2721/2025.20955.

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With the development of deepfake technology, the use of this technology to forge videos and images has caused serious privacy and legal problems in society. In order to solve these problems, deepfake detection is required. In this paper, the generation and detection techniques of deepfakes in recent years are studied. First, the principles of deepfake generation technology are briefly introduced, including Generative Adversarial Networks (GAN) based and autoencoder. Then, this paper focuses on the detection techniques of deepfakes, classifies them based on the principles of each method, and su

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Raza, Ali, Kashif Munir, and Mubarak Almutairi. "A Novel Deep Learning Approach for Deepfake Image Detection." Applied Sciences 12, no. 19 (2022): 9820. http://dx.doi.org/10.3390/app12199820.

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Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Ki

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Shilpa, K. C., B. P. Poornima, R. Pai Rajath, Harmain Khan Rakeen, N. S. Shreya, and A. Suchith. "A Comprehensive Review on Deep Fake Detection in Videos." Journal of Advancement in Architectures for Computer Vision 1, no. 1 (2025): 33–44. https://doi.org/10.5281/zenodo.15118896.

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<em>The last few decades have seen a significant rise in Artificial Intelligence (AI) and Machine Learning (ML), promoting the development of deepfake technology. Deepfakes are synthetic media created using AI techniques, altering audio, images, and videos to appear authentic but are fabricated. Employing concepts like Generative Adversarial Networks (GANs), deepfake creation involves a competitive process where one model produces forgeries while another aims to identify them. The consequences of deepfakes are extensive, ranging from misinformation campaigns by terrorist organizations to indiv

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Singh, Preeti, Khyati Chaudhary, Gopal Chaudhary, Manju Khari, and Bharat Rawal. "A Machine Learning Approach to Detecting Deepfake Videos: An Investigation of Feature Extraction Techniques." Journal of Cybersecurity and Information Management 9, no. 2 (2022): 42–50. http://dx.doi.org/10.54216/jcim.090204.

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Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our

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Prayas, Chaudhary, Jain Prasuk, Kumar Bhardwaj Rajnish, Tyagi Vasu, and Jalhotra Sonika. "Deepfake Video Face Detection using Deep Learning." Recent Trends in Information Technology and its Application 8, no. 3 (2025): 19–26. https://doi.org/10.5281/zenodo.15429570.

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<em>The proliferation of deepfake technology, which uses artificial intelligence to create highly realistic synthetic videos and images, poses major risks to privacy, security, and confidence in digital platforms. Traditional approaches to achieving these properties are often limited by the complexity of the algorithms. This paper proposes a novel approach for deepfake face detection using Deep Learning (DL) suited for sequential data analysis. Our method leverages the temporal dependencies and patterns inherent in video sequences to identify subtle inconsistencies and artifacts introduced by

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Qureshi, Shavez Mushtaq, Atif Saeed, Sultan H. Almotiri, Farooq Ahmad, and Mohammed A. Al Ghamdi. "Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media." PeerJ Computer Science 10 (May 27, 2024): e2037. http://dx.doi.org/10.7717/peerj-cs.2037.

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The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized. This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes. A rigorous methodology is followed, consolidating findings from recent publications on deepfake detection innovation. Prevalent datasets that underpin new techniques are analyzed. The effectiveness and limitations of estab

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Pallavi, Abburi. "DeepFake Detection for Human Face Images and Videos: A Comprehensive Survey." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47887.

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Abstract - With the growing sophistication of deep learning and generative models, the creation of synthetic media such as DeepFakes has become increasingly convincing and widespread. DeepFakes pose serious threats across multiple sectors, from political misinformation to personal identity theft. This paper reviews the current progress in DeepFake detection techniques focused on human facial images and video content. It categorizes detection methodologies into feature-based approaches, deep learning models, biological signal analysis, and multimodal systems. Additionally, it discusses benchmar

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Rajagopal, Tendral, Velayutham Chandrashekaran, and Vignesh Ilango. "Unmasking the Deepfake Infocalypse: Debunking Manufactured Misinformation with a Prototype Model in the AI Era “Seeing and hearing, no longer believing.”." Journal of Communication and Management 2, no. 04 (2023): 230–37. http://dx.doi.org/10.58966/jcm2023243.

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Machine learning and artificial intelligence in Journalism are aid and not a replacement or challenge to a journalist’s ability. Artificial intelligence-backed fake news characterized by misinformation and disinformation is the new emerging threat in our broken information ecosystem. Deepfakes erode trust in visual evidence, making it increasingly challenging to discern real from fake. Deepfakes are an increasing cause for concern since they can be used to propagate false information, fabricate news, or deceive people. While Artificial intelligence is used to create deepfakes, the same technol

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Nisha, Rose, K. Lijina, Jumana Fathimathul, K. Sayana, and V. Gopichandana. "Multimedia Deepfake Detection." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 3 (2025): 1–5. https://doi.org/10.5281/zenodo.15471531.

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<em>In tech-enabled communities, social media allows users to access multimedia content easily. With recent advancements in computer vision and natural language processing, machine learning (ML) and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create synthetic media of a person or use some person’s contents to fit other environments. Deepfakes are fake media generated using advanced tools and applications, which may mislead people and create an issue of trust within communities. Detecting fakes is crucial an

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Ghariwala, Love. "Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2982–86. https://doi.org/10.22214/ijraset.2025.67997.

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The rise of Artificial Intelligence (AI) has opened up new possibilities, but it also brings significant challenges. Deepfake technology, which creates realistic fake videos, raises concerns about privacy, identity, and consent. This paper explores the impacts of deepfakes and suggests solutions to mitigate their negative effects. Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes cru

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Garcia, Jan Mark. "Exploring Deepfakes and Effective Prevention Strategies: A Critical Review." Psychology and Education: A Multidisciplinary Journal 33, no. 1 (2025): 93–96. https://doi.org/10.70838/pemj.330107.

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Deepfake technology, powered by artificial intelligence and deep learning, has rapidly advanced, enabling the creation of highly realistic synthetic media. While it presents opportunities in entertainment and creative applications, deepfakes pose significant risks, including misinformation, identity fraud, and threats to privacy and national security. This study explores the evolution of deepfake technology, its implications, and current detection techniques. Existing methods for deepfake detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative

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Singh, Parminder. "A Survey of Deepfake Detection Methods: Innovations, Accuracy, and Future Directions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem37000.

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Deepfake technology has emerged as a significant challenge in digital media, posing risks related to misinformation and identity theft. This paper provides a comprehensive review of deepfake detection techniques, highlighting advancements in traditional machine learning, deep learning models, hybrid approaches, and attention mechanisms. We evaluate the effectiveness of various methods based on accuracy, computational efficiency, and practical applicability, using key datasets and benchmarking systems. Our review underscores the progress made in detecting deepfakes and identifies areas for futu

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Ok, Arifa. "Deepfake Detection Using Convolutional Vision Transformers and Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 770–76. https://doi.org/10.22214/ijraset.2025.67377.

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Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detec

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Deressa, Deressa Wodajo, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar, and Glenn Van Wallendael. "GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer." Applied Sciences 15, no. 12 (2025): 6622. https://doi.org/10.3390/app15126622.

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Deepfakes have raised significant concerns due to their potential to spread false information and compromise the integrity of digital media. Current deepfake detection models often struggle to generalize across a diverse range of deepfake generation techniques and video content. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes an Autoencoder and Variational Autoencoder to learn from latent data distributions. By learning from the visual

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Wildan, Jameel Hadi, Malallah Kadhem Suhad, and Rodhan Abbas Ayad. "A survey of deepfakes in terms of deep learning and multimedia forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4408–14. https://doi.org/10.11591/ijece.v12i4.pp4408-4414.

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a d

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Kumar K N, Mr Anil. "Fake Image Detection Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47455.

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Abstract — Design and implement a deepfake detection system capable of distinguishing authentic images from deepfake images that involve facial manipulation. This system should identify manipulated faces, thereby mitigating the harmful effects of deepfake technology. With the rapid advancement of image editing tools and generative technologies like deepfakes, the spread of manipulated or fake images has become a serious concern in areas ranging from social media to national security. Traditional methods of image verification are often inadequate due to the sophistication of modern forgeries.

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Singh, Dr Viomesh, Bhavesh Agone, Aryan More, Aryan Mengawade, Atharva Deshmukh, and Atharva Badgujar. "SAVANA- A Robust Framework for Deepfake Video Detection and Hybrid Double Paraphrasing with Probabilistic Analysis Approach for AI Text Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2074–83. http://dx.doi.org/10.22214/ijraset.2024.65526.

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Abstract: As the generative AI has advanced with a great speed, the need to detect AI-generated content, including text and deepfake media, also increased. This research work proposes a hybrid detection method that includes double paraphrasing-based consistency checks, coupled with probabilistic content analysis through natural language processing and machine learning algorithms for text and advanced deepfake detection techniques for media. Our system hybridizes the double paraphrasing framework of SAVANA with probabilistic analysis toward high accuracy on AI-text detection in forms such as DO

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Gosavi, Prof Amol. "Deepfake Video Face Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5840–47. https://doi.org/10.22214/ijraset.2025.69233.

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The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This rese

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Lee, Eun-Gi, Isack Lee, and Seok-Bong Yoo. "ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection." Mathematics 11, no. 18 (2023): 3952. http://dx.doi.org/10.3390/math11183952.

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Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream c

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Jagdale, Anushka, Vanshika Kubde, Rahul Kortikar, Prof Aparna V. Mote, and Prof Nitisha Rajgure. "DeepFake Image Detection: Fake Image Detection using CNNs and GANs Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38628.

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Deep learning is a powerful and versatile technique that has seen extensive applications in areas such as natural language processing, machine learning, and computer vision. Among its most recent applications is the generation of deepfakes, which are high-quality, realistic altered videos or images that have garnered significant attention. While innovative uses of deepfake technology are being explored, its potential for misuse has raised serious concerns. Harmful applications, such as spreading fake news, creating celebrity pornography, financial fraud, and revenge pornography, have become in

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Dr.A.Shaji, George, and George A.S.Hovan. "Deepfakes: The Evolution of Hyper realistic Media Manipulation." Partners Universal Innovative Research Publication (PUIRP) 01, no. 02 (2023): 58–74. https://doi.org/10.5281/zenodo.10148558.

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Deepfakes, synthetic media created using artificial intelligence and machine learning techniques, allow for the creation of highly realistic fake videos and audio recordings. As deepfake technology has rapidly advanced in recent years, the potential for its misuse in disinformation campaigns, fraud, and other forms of deception has grown exponentially. This paper explores the current state and trajectory of deepfake technology, emerging safeguards designed to detect deepfakes, and the critical role of education and skepticism in inoculating society against their harms. The paper begins by prov

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Guarnera, Luca, Oliver Giudice, Francesco Guarnera, et al. "The Face Deepfake Detection Challenge." Journal of Imaging 8, no. 10 (2022): 263. http://dx.doi.org/10.3390/jimaging8100263.

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Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable o

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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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Pankhuri, Tripathi, Singh Shikha, Nishad Anubhav, and Siddiqui Farheen. "Fraudshield – Deepfake Detection Tools." International Journal of Engineering and Management Research 15, no. 2 (2025): 47–51. https://doi.org/10.5281/zenodo.15314707.

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FraudShield is a web application designed to detect and mitigate the impact of deepfakes, ensuring content authenticity and integrity. With the rise of image manipulation and deepfake videos, detecting fraudulent activities has become increasingly critical. This project introduces a hybrid detection system that integrates Convolutional Neural Networks (CNNs) to identify morphed images and manipulated content. The framework leverages machine learning techniques to detect tampered facial features, artifacts, and inconsistencies in deepfake videos and images. The CNN component analyzes visual fea

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Paul, Rajdeep. "An API-Integrated CNN–RNN Framework for Scalable Deepfake Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42998.

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Deepfake technology has been rapidly advancing, posing significant threats to media authenticity, cybersecurity, and public trust. It's a serious threat to identity verification in the digital medium. To tackle and solve this serious problem a deepfake detection approach is taken. A Convolutional Neural Network (CNN) algorithm named Resnext and a Recurrent Neural Network (RNN) algorithm named Long Term Short Memory (LTSM) is used to train a deepfake detection model. The whole approach and process is discussed. The model accuracy obtained is 91% using Celeb-Df dataset, Then the integration conc

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Shahzad, Hina Fatima, Furqan Rustam, Emmanuel Soriano Flores, Juan Luís Vidal Mazón, Isabel de la Torre Diez, and Imran Ashraf. "A Review of Image Processing Techniques for Deepfakes." Sensors 22, no. 12 (2022): 4556. http://dx.doi.org/10.3390/s22124556.

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complement

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A. Abu-Ein, Ashraf, Obaida M. Al-Hazaimeh, Alaa M. Dawood, and Andraws I. Swidan. "Analysis of the current state of deepfake techniques-creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1659-1667.

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algo

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Ashraf, A. Abu-Ein1, M. Al-Hazaimeh2 Obaida, M. Dawood3 Alaa, and I. Swidan3 Andraws. "Analysis of the current state of deepfake techniques-creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659–67. https://doi.org/10.11591/ijeecs.v28.i3.pp1659-1667.

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Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algo

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Diljith, M. S., C. P. Emilyn, Afitha Abu T. Fathimathul, and KS Salkala. "Deepfake Technology: An Overview, Applications, Detection, and Future Challenges." Journal of Advancement in Architectures for Computer Vision 1, no. 1 (2025): 45–53. https://doi.org/10.5281/zenodo.15152176.

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<em>Deepfake technology, powered by artificial intelligence, has revolutionized digital media by enabling the creation of highly realistic synthetic videos, images, and audio. While it offers numerous benefits in fields such as entertainment, education, and accessibility, deepfake technology also raises significant ethical, legal, and security concerns. This report explores the methods used to generate deepfakes, including Generative Adversarial Networks (GANs) and autoencoders, and highlights key deepfake techniques such as face-swapping, lip-syncing, and voice cloning. It further examines bo

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Gupta, Gourav, Kiran Raja, Manish Gupta, Tony Jan, Scott Thompson Whiteside, and Mukesh Prasad. "A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods." Electronics 13, no. 1 (2023): 95. http://dx.doi.org/10.3390/electronics13010095.

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Recent advances in Generative Artificial Intelligence (AI) have increased the possibility of generating hyper-realistic DeepFake videos or images to cause serious harm to vulnerable children, individuals, and society at large with misinformation. To overcome this serious problem, many researchers have attempted to detect DeepFakes using advanced machine learning techniques and advanced fusion techniques. This paper presents a detailed review of past and present DeepFake detection methods with a particular focus on media-modality fusion and machine learning. This paper also provides detailed in

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AL-KHAZRAJI, Samer Hussain, Hassan Hadi SALEH, Adil Ibrahim KHALID, and Israa Adnan MISHKHAL. "Impact of Deepfake Technology on Social Media: Detection, Misinformation and Societal Implications." Eurasia Proceedings of Science Technology Engineering and Mathematics 23 (October 16, 2023): 429–41. http://dx.doi.org/10.55549/epstem.1371792.

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Deepfake technology, which allows the manipulation and fabrication of audio, video, and images, has gained significant attention due to its potential to deceive and manipulate. As deepfakes proliferate on social media platforms, understanding their impact becomes crucial. This research investigates the detection, misinformation, and societal implications of deepfake technology on social media. Through a comprehensive literature review, the study examines the development and capabilities of deepfakes, existing detection techniques, and challenges in identifying them. The role of deepfakes in sp

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Kumari, Prerna, and Vikas Kumar. "Deepfake Detection." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 356–58. http://dx.doi.org/10.21275/sr24606012528.

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Sharma, Ankita. "RESILIENCE OF NETWORK PROTOCOLS TO DEEPFAKE DETECTION TRAFFIC." International Research Journal of Computer Science 09, no. 08 (2022): 342–47. http://dx.doi.org/10.26562/irjcs.2022.v0908.36.

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This research analyzes the robustness of current multimedia protocols in managing traffic produced by real-time, high-volume deepfake detection. The widespread occurrence of deepfakes on online platforms necessitates swift and precise detection algorithms for multimedia data, highlighting the demand for optimized network protocols. This paper assesses the existing functionalities of multimedia protocols, including RTP and RTSP, in facilitating deepfake detection and proposes improvements to enhance robustness, speed, and accuracy. The results underscore the necessary protocol modifications to

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Vakdevi, Vallabhaneni, Dheeraj T., Chandra Sekhar B., et al. "A Comprehensive Review of Deepfake Detection Pertaining to Images, Videos, Audio, and News using Deep Learning Techniques." Engineering and Technology Journal 10, no. 04 (2025): 4535–43. https://doi.org/10.5281/zenodo.15255522.

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Deepfakes, which are synthetic media realistic in nature generated using artificial intelligence (AI); pose a significant threat to individuals and society. The rapid advancement of deepfake technology has led to the creation of highly realistic synthetic content covering images, videos, audio, and news. While deepfake applications offer creative possibilities, their misuse for misinformation, identity fraud, and cybersecurity threats necessitates robust detection methods. Deepfake crimes are rising daily, wherein deepfake media detection has become a big challenge and has high claim in digita

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Mrs. Sushma D. S, Sumanth T.C, Mehraj, Likhith.R, and Lohith T. R. "A Hybrid Approach to Deep Fake Detection Using Error Level Analysis." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 01 (2025): 98–102. https://doi.org/10.47392/irjaeh.2025.0013.

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The rapid advancement of ‘deepfake’ video technology— which uses deep learning artificial intelligence algorithms to create fake videos that look real—has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake among a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compar

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S, Mrs Prajwal. "DeepFake Image Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30215.

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The growth of deepfakes in today’s digital environ- ment raises significant doubts regarding the genuineness and dependability of the content found. To overcome this new challenge, Developing an effective method in the context of detection of deep images. In this study, we conduct a comparative analysis of three varied convolutional neural networks (CNNs) for deepfake image detection. Our experimental results highlight the strengths and weaknesses of each CNN architecture. We deliberate on the consequences of our results in the context of deep image perception and show which models may be bett

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Law Kian Seng, NORMAISHARAH MAMAT, Hafiza Abas, and Wan Noor Hamiza Wan Ali. "AI Integrity Solutions for Deepfake Identification and Prevention." Open International Journal of Informatics 12, no. 1 (2024): 35–46. http://dx.doi.org/10.11113/oiji2024.12n1.297.

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The increasing complexity of deepfake technology has sparked significant worries over individual privacy, the spread of false information, and deficiencies in cybersecurity. Deepfakes have the ability to effectively modify audio and visual content, resulting in a growing challenge to differentiate between real and fake content. To address this critical challenge, the study is conducting a survey to reveal a broad range of perspectives on the familiarity, encounters, and concerns related to deepfake technology. In addition, the study evaluates the effectiveness of current strategies in addressi

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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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