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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 (May 31, 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 potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
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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 (May 6, 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 techniques using neural networks. Additionally, a detailed study of the different technologies used in deepfake detection is provided. This will surely benefit researchers in this area because it will include new cutting-edge methods for detecting fake videos or images on social networks. Moreover, it will make it easy for us to compare what others have done in their papers by explaining how they came up with their models or what information was employed for training them. Key Words: Deep Learning, Fake Detection, Neural Networks, Social Networks
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D P, Gurukiran. "Deep Fake Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 17, 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 technology aims to offer real-time detection capabilities by utilizing sophisticated neural networks and machine learning techniques. This will aid in the continuous endeavors to tackle the widespread occurrence of deepfakes in digital media. Key Words: Deep Fake Detection, Deep Learning, Media Integrity, Misinformation, Neural Networks
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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 (April 17, 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 with deep fake misuse while leveraging its potential for positive applications.
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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 (April 30, 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 the complexity of training algorithms to spot them. In this work, we propose a detection approach using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Our system leverages a CNN to extract features at the frame level, which are then used to train an RNN. The RNN learns to classify whether a video has been manipulated, detecting temporal inconsistencies between frames introduced by DF creation tools. We evaluate our approach against a large set of fake videos collected from standard datasets and demonstrate competitive results using a simple architecture.
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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 (March 30, 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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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 (March 30, 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 more deepfakes are spreading, we really need better ways to find and prevent them. Deepfake involves creation of highly realistic images and videos and misuse them for spreading fake news, defaming individuals, and possess a significant threat to the integrity of digital content. Our project “Deep Learning Approaches for Robust Deep Fake Detection” aims to address this critical issue by developing a robust system for identification and localization of deep fake content by using ‘Densenet121’ model. This proposed framework seamlessly integrates forgery detection and localization. The dataset used in this project is “140k Real and Fake Faces”, and it consists of 70k real faces from Flickr dataset collected by Nvidia and 70k fake faces sampled from the 1 million Fake faces generated by StyleGAN. For localization purpose, we use GRAD-CAM method to accurately identify the morphed regions. Overall, our goal is to make deepfake detection more effective and reliable in today’s digital landscape.
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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-quality Deep-Fakes was utilised to test models. The results of both with and without Transfer Learning were analysed. Finally, the trained models were used to spot well-known deep-fakes of former US President Barack Obama and well-known actor Tom Cruise. A comparison study was performed on all three models. The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.
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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 (September 23, 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, therefore detection of deep fake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and also consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and Wild Deep fake. The accuracy for FFHQ datasets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82% respectively.
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ST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán, and Trojovský Pavel. "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, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
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Rai, Mr Yogesh. "Deepfake Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 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 train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies
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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 (March 22, 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 combination of Facfo-rensic++ and DeepFake Detection Challenge Dataset. This proposed system gives the highest accuracy of 97%.
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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 (February 8, 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 deep fake detection is given in this abstract. In this article, we go over the fundamental ideas behind deepfake creation and investigate the many approaches used to spot and stop the spread of fake news. Methods include sophisticated machine learning algorithms trained on enormous datasets of real and fake media, as well as conventional forensic investigation. We explore the principal characteristics and artifacts that differentiate authentic video from deepfakes, such as disparities in audio-visual synchronization, aberrant eye movements, and inconsistent facial emotions. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), two deep learning frameworks, have been used by researchers to create sophisticated detection models that can recognize minute modifications in multimedia information. The fast developments in deep fake generating techniques, however, continue to exceed efforts in detection and mitigation, making deep fake detection a daunting problem. The issue is made worse by the democratization of deepfake technology and its accessibility to non-experts, which calls for creative solutions and multidisciplinary cooperation to counter this expanding danger. Keywords: convolutional neural network,, generative adversarial network, deep fake ,long short term memory,
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Journal, IJSREM. "FAKE REVIEW DETECTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 4, 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 proposed model integrates word embeddings and attention mechanisms to capture intricate patterns and dependencies within review texts. Furthermore, the research leverages a dataset of labeled reviews, distinguishing between genuine and fake reviews using various linguistic, behavioral, and sentiment-based features. The model is trained, validated, and fine-tuned using this dataset to enhance its ability to generalize across different review platforms and domains. Experimental results demonstrate the efficacy of the proposed deep learning model in accuratelyidentifying fake reviews, achieving state-of-the-art performance metrics such as precision, recall, and F1-score. KEYWORDS: Data mining, Neural Network, Recurrent neural network, Tokenization,Lemmatization, Clustering, Anamoly detection, Text Classification.
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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 (December 31, 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 intelligence play an important role in solving such issues. Thus, detecting fake images is a critical problem that must be controlled and to prevent these numerous harmful effects. This research proposed utilizing the most popular algorithm in deep learning is (Convolution Neural Network) to detect the fake images.
The first steps includes a preprocessing which start with converting images from RGB to YCbCr color space, after that entering the Gamma correction. finally extract edge detection by entering the Canny filter on them. After that, utilizing two different method of detection by applying (Convolution Neural Network with Principal Component Analysis) and (Convolution Neural Network without Principal Component Analysis) as a classifiers.
The results reveal that the use of CNN with PCA in this research results in acceptable accuracy. In contrast, using CNN only gave the highest level of accuracy in detecting manipulated images.
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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 (December 1, 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 paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%.
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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 (October 7, 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 authentic and fraudulent videos.
The objective of this work is to give a summary of different approaches for Deep Fake creation and to provide an overview of LSTM algorithms for deep fake video detection.
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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 (April 28, 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 sequences, harnessing the unique strengths of LSTM in capturing sequential information. We demonstrate the effectiveness of our methodology by training the LSTM network on a diverse dataset comprising both real and deep fake videos. The network’s ability to learn temporal dependencies and identify inconsistencies in facial expressions, eye movements, and other subtle cues allows it to distinguish between genuine and manipulated content. To further enhance the accuracy and robustness of our deep fake face detection system, we integrate pre-processing techniques for frame- level analysis, such as optical flow computation and facial landmarks extraction. Additionally, we employ a comprehensive ensemble of LSTM models and other machine learning algorithms to improve the overall detection performance. In our experiments, we evaluate the LSTM-based deep fake detection system on a large-scale dataset of both known and unseen deep fake videos, achieving high detection accuracy and low false positive rates. We also compare our approach with existing methods, demonstrating its superiority in terms of robustness and generalization. The results of this study signify the potential of LSTM-based models for mitigating the adverse effects of deep fake content on society. As deep fake technology continues to evolve, our approach showcases a promising step towards combating the dissemination of deceptive multimedia, promoting media integrity, and upholding trust in visual information. Keywords: LSTM Networks, Recurrent Neural Network, Optical flow computation, Facial landmarks extraction, False positive rates.
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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 (April 27, 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 paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning-based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake
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Pote, Asst Prof Rajashri. "Deep Fake Detecting System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 1579–82. http://dx.doi.org/10.22214/ijraset.2024.59748.
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Abstract: Over the past few decades, there have been rapid breakthroughs in AI, machine learning, and deep learning, which have led to the development of new tools and methodologies for manipulating multimedia. Technology has generally been employed for beneficial purposes, such as education and entertainment, but dishonest users have also exploited it for darker or illicit purposes. For example, incredibly realistic-looking, well-produced fake movies, images, or audio have been made to spread false information, incite political unrest and hatred, or even to harass and blackmail people. The extremely replicated, realistic, and edited videos have been dubbed as "Deepfake" in recent times. Since then, several approaches to resolving the problems raised by Deepfake have been described in length in the literature. We conduct a systematic literature review (SLR) in this work to give a current synopsis of the Deepfake detection research projects. We provide an overview of 112 pertinent publications from 2018 to 2020 that showcased various methodologies. For analysis, we divide them into four categories: deep learning-based approaches, traditional machine learning-based approaches, statistical-based approaches, and blockchain-based approaches. We also evaluate the pattern recognition performance of several algorithms on various datasets, and we find that deep learningbased approaches outperform other approaches in Deepfake detection
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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 (September 30, 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 spotting various sorts of fake news. We can tell if the news is genuine or created if we have encountered fake or authentic news before. We may use a number of models to understand social media news. This is a donation in two ways. We must first give datasets containing both fake and accurate news and conduct multiple experiments before developing a false news detector. Various machine learning techniques are used to categorize the data. Random Forest, Logistic Regression, Naives Bayes, Gradient Boost and Decision Tree techniques are used and compared. It was found that Gradient Boost has the best accuracy.
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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 associated with limitations in the volume and quality of data, as well as the lack of efficiency of tools for detecting complex types of fakes. The author analyses neural network-based applications and tools and draws conclusions about their effectiveness and suitability for different types of data and fake detection tasks. The study found that machine and deep learning models, as well as adversarial learning methods and special tools for detecting fake media, are effective in detecting fakes. However, the effectiveness and accuracy of these methods and tools can be affected by factors such as data quality, methods used for training and evaluation, and the complexity of the fake media being detected. Based on the analysis of training methods and neural network characteristics, the advantages and disadvantages of fake news detection are identified. Ongoing research and development in this area is crucial to improve the accuracy and reliability of these methods and tools for fake news detection.
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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 (July 31, 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 techniques are combined with natural language processing techniques. We gathered information from open sources, such as Kaggle. Following that, we used NLP techniques to preprocess and analyze the data. In the form of exploratory data analysis, there is a detailed representation of graphical plots (EDA). To gain a better understanding of the data through more precise statistics.
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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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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 the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches.
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NAGAGOPIRAJU, Dr V., Kancharla Ayyappa, Pallabothula Anshulalitha, Jillalamudi Srikanth, and Kakumanu Tharun Teja. "A EFFCIET DEEP FAKE FACE DETECTION USING DEEP INCEPTION NET LEARNING ALGORITHM." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (March 4, 2024): 138–41. http://dx.doi.org/10.61841/turcomat.v15i1.14555.
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A Deep Fake Is Digital Manipulation Techniques That Use Deep Learning to Produce Deep Fake (Misleading) Images and Videos. Identifying Deep Fake Images Is the Most Difficult Part of Finding the Original. Due To the Increasing Reputation of Deep Fakes, Identifying Original Images and Videos Is More Crucial to Detect Manipulated Videos. This Paper Studies and Experiments with Different Methods That Can Be Used to Detect Fake and Real Images and Videos. The Convolutional Neural Network (Cnn) Algorithm Named Inception Net Has Been Used to Identify Deep Fakes. A Comparative Analysis Was Performed in This Work Based on Various Convolutional Networks. This Work Uses the Dataset from Kaggle With 401 Videos of Train Sample And 3745 Images Were Generated by Augmentation Process. The Results Were Evaluated with The Metrics Like Accuracy and Confusion Matrix. The Results of The Proposed Model Produces Better Results in Terms of Accuracy With 93% On Identifying Deep Fake Images and Videos.
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Fang, Kairui. "Deep Learning Techniques for Fake News Detection." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 511–18. http://dx.doi.org/10.54097/hset.v16i.2638.
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Social media has recently become the primary source for people to consume news. Plenty of users prefer to go to social media apps such as Twitter, Facebook, and Snapchat to obtain the latest social events and news. Meanwhile, traditional media is emulating the new media to post their news on the aforementioned apps. This prevalence is a double-edged sword, for the advantage is that users can easily gain access to the news articles they look for on social media. However, it also provides an ideal platform for fake news propagation. The spread of fake news is extremely fast on social media and can cause adverse effects in real life. The unregimented, incomplete censorship and the absence of fact-checking processes make fake news easy to propagate and hard to control. Therefore, fake news detection on social media has become a trending topic that draws tremendous attention, as shown in figure 1. Nevertheless, as pundits dig into the realm of deep learning, some of the studies utilize deep neural networks (DNN) to build frameworks that would help detect fake news. Although impressive progress on the topic has been made, the lack of a review dissertation that summarizes and synthesizes the overall development of the study would be problematic. Hence, this paper aims to summarize different models implemented in recent studies that improve the veracity of fake news detection.
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Meenakshi A Thalor and Mayuri Garad. "Fake News Detection Using MultiChannel Deep Neural Networks." International Journal of Integrated Science and Technology 1, no. 5 (November 22, 2023): 585–94. http://dx.doi.org/10.59890/ijist.v1i5.684.
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Fake news has become a pervasive issue in today's digital age, posing significant challenges to information integrity and trustworthiness. In this study, we propose a novel approach for the detection of fake news using MultiChannel Deep Neural Networks (MC-DNNs). Our research aims to address the limitations of traditional fake news detection methods by leveraging the power of deep learning and multiple data sources.
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Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. "Deep Fake Image Detection Based on Pairwise Learning." Applied Sciences 10, no. 1 (January 3, 2020): 370. http://dx.doi.org/10.3390/app10010370.
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Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
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Feng, Yuran. "Misreporting and Fake News Detection Techniques on the Social Media Platform." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 142–52. http://dx.doi.org/10.54097/hset.v12i.1417.
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One of the major concerns nowadays is the rapid spreading of fake news or unverified information on all kinds of social media. Misinformation and disinformation on the digital media of news distribution have brought significant negative impacts to our community, which the traditional techniques can no longer detect and deal with it effectively. It is urgent to squelch fake news immediately to limit its impact on the economy and society. As deep learning techniques continue developing in recent decades, scholars successfully deployed deep neural networks on fake news detection tasks. The first noticeable thing is to admit that the fake news detection task has made significant accomplishments as fast as we hoped. It is necessary to study further and broadly review the state-of-the-art fake news detection approaches. In this review paper, we survey several distinct deep learning techniques and provide a comprehensive review of automatic fake news detection classification tasks and the datasets and models used, demonstrating the performance evaluation on different approaches. We have also analyzed the potential challenge we encountered in fake news detection.
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Aslam, Nida, Irfan Ullah Khan, Farah Salem Alotaibi, Lama Abdulaziz Aldaej, and Asma Khaled Aldubaikil. "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection." Complexity 2021 (April 14, 2021): 1–8. http://dx.doi.org/10.1155/2021/5557784.
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Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.
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Li, Jun, Wentao Jiang, Jianyi Zhang, Yanhua Shao, and Wei Zhu. "Fake User Detection Based on Multi-Model Joint Representation." Information 15, no. 5 (May 9, 2024): 266. http://dx.doi.org/10.3390/info15050266.
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The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.
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Shahar, Hadas, and Hagit Hel-Or. "Fake Video Detection Using Facial Color." Color and Imaging Conference 2020, no. 28 (November 4, 2020): 175–80. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.27.
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The field of image forgery is widely studied, and with the recent introduction of deep networks based image synthesis, detection of fake image sequences has increased the challenge. Specifically, detecting spoofing attacks is of grave importance. In this study we exploit the minute changes in facial color of human faces in videos to determine real from fake videos. Even when idle, human skin color changes with sub-dermal blood flow, these changes are enhanced under stress and emotion. We show that extracting facial color along a video sequence can serve as a feature for training deep neural networks to successfully determine fake vs real face sequences.
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Haqi Al-Tai, Mohammed, Bashar M. Nema, and Ali Al-Sherbaz. "Deep Learning for Fake News Detection: Literature Review." Al-Mustansiriyah Journal of Science 34, no. 2 (June 30, 2023): 70–81. http://dx.doi.org/10.23851/mjs.v34i2.1292.
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The use of Deep Learning (DL) for identifying false or misleading information, known as fake news, is a growing area of research. Deep learning, a form of machine learning that utilizes algorithms to learn from large data sets, has shown promise in detecting fake news. The spread of fake news can cause significant harm to society economically, politically, and socially, and it has become increasingly important to find ways to detect and stop its spread. This paper examines current studies that use deep learning methods, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), as well as the multi-model approach, to detect fake news. It also looks at the use of word embedding models to convert text to vector representations and the datasets used for training models. Furthermore, the paper discusses the use of the attention mechanism in conjunction with deep learning to process sequential data.
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Patil, Priyadarshini, Vipul Deshpande, Vishal Malge, and Abhishek Bevinmanchi. "Fake Face Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 519–22. http://dx.doi.org/10.22214/ijraset.2022.45829.
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Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and true. Regarding the models tested so far, the VGG models have the best training accuracy (86%) on VGG-16, while VGG-16 shows an excellent test set. accuracy with 10 epochs or less, which is competitively better than all other methods. The outputs of these models were examined to find out exactly
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Ali, Abdullah Marish, Fuad A. Ghaleb, Bander Ali Saleh Al-Rimy, Fawaz Jaber Alsolami, and Asif Irshad Khan. "Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique." Sensors 22, no. 18 (September 15, 2022): 6970. http://dx.doi.org/10.3390/s22186970.
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Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.
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Alkishri, Wasin, Dr Setyawan Widyarto, and Dr Jabar H. Yousif. "Evaluating the Effectiveness of a Gan Fingerprint Removal Approach in Fooling Deepfake Face Detection." Journal of Internet Services and Information Security 14, no. 1 (March 2, 2024): 85–103. http://dx.doi.org/10.58346/jisis.2024.i1.006.
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Deep neural networks are able to generate stunningly realistic images, making it easy to fool both technology and humans into distinguishing real images from fake ones. Generative Adversarial Networks (GANs) play a significant role in these successes (GANs). Various studies have shown that combining features from different domains can produce effective results. However, the challenges lie in detecting these fake images, especially when modifications or removal of GAN components are involved. In this research, we analyse the high-frequency Fourier modes of real and deep network-generated images and show that Images generated by deep networks share an observable, systematic shortcoming when it comes to reproducing their high-frequency features. We illustrate how eliminating the GAN fingerprint in modified pictures' frequency and spatial spectrum might affect deep-fake detection approaches. In-depth review of the latest research on the GAN-Based Artifacts Detection Method. We empirically assess our approach to the CNN detection model using style GAN structures 140k datasets of Real and Fake Faces. Our method has dramatically reduced the detection rate of fake images by 50%. In our study, we found that adversaries are able to remove the fingerprints of GANs, making it difficult to detect the generated images. This result confirms the lack of robustness of current algorithms and the need for further research in this area.
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Kale, Prachi. "Forensic Verification and Detection of Fake Video using Deep Fake Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 2789–94. http://dx.doi.org/10.22214/ijraset.2021.35599.
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In the course of the most recen years, the ascent in cell phones and interpersonal organizations has made computerized pictures and recordings basic advanced articles. per reports, right around two billion pictures are transferred every day on the web. This gigantic utilization of computerized pictures has been trailed by an increment of methods to change picture substance, utilizing altering programming like Photoshop for instance. Counterfeit recordings and pictures made by deepFake methods turned into a decent open issue as of late. These days a few procedures for facial control in recordings are effectively evolved like FaceSwap, deepFake, and so on On one side, this innovative progression increment degree to new regions (e.g., film making, special visualization, visual expressions, and so on) On the contrary side, repudiating, it likewise expands the advantage inside the age of video frauds by malignant clients. In this manner by utilizing profound learning strategies we can distinguish the video is phony or not. to recognize these malevolent pictures, we are visiting foster a framework which will naturally identify and survey the trustworthiness of advanced visual media is in this way crucial. Deepfake could be a procedure for human picture union upheld AI, i.e., to superimpose the predominant (source) pictures or recordings onto objective pictures or recordings utilizing neural organizations (NNs). Deepfake aficionados are utilizing NNs to give persuading face trades. Deepfakes are a sort of video or picture imitation created to spread deception, attack protection, and veil the truth utilizing cutting edge innovations like prepared calculations, profound learning applications, and figuring. they need become an irritation to online media clients by distributing counterfeit recordings made by melding a big name's face over a precise video. The effect of deepFakes is disturbing, with lawmakers, senior corporate officials, and world pioneers being focused by loathsome entertainers. A way to deal with distinguish deepFake recordings of legislators utilizing transient consecutive edges is proposed. The proposed approach utilizes the strong video to separate the edges at the essential level followed by a profound profundity based convolutional long memory model to recognize the phony casings at the subsequent level. Additionally, the proposed model is assessed on our recently gathered ground truth dataset of produced recordings utilizing source and objective video edges of renowned lawmakers. Trial results exhibit the viability of our strategy.
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Lin, Yih-Kai, and Hao-Lun Sun. "Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach." Electronics 12, no. 6 (March 16, 2023): 1417. http://dx.doi.org/10.3390/electronics12061417.
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There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.
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M. Fouad, Khaled, Sahar F. Sabbeh, and Walaa Medhat. "Arabic Fake News Detection Using Deep Learning." Computers, Materials & Continua 71, no. 2 (2022): 3647–65. http://dx.doi.org/10.32604/cmc.2022.021449.
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Dcruz, Juliet, Mariya Eldhose, Mariya Jose, and Bonia Jose. "FAKE INDIAN CURRENCY DETECTION USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 5, no. 1 (May 31, 2020): 720–24. http://dx.doi.org/10.33564/ijeast.2020.v05i01.127.
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Wang, Rong. "Fake News Detection based on Deep Learning." Frontiers in Computing and Intelligent Systems 4, no. 1 (June 6, 2023): 105–8. http://dx.doi.org/10.54097/fcis.v4i1.9479.
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The rapid popularization of the Internet has broken the professional threshold of information dissemination, enabling more and more people to easily obtain information, share and express views through social media, which has greatly enriched people's daily life. However, due to the huge number of users of social media, false news fabricated for various purposes is emerging in endlessly. Moreover, with the progress of technology, false news is no longer simply spread in the form of text, but more spread through the combination of text, pictures and video, which greatly increases the confusion of false news. The experiment in this paper is based on tensorflow to detect false news. During the experiment, LR was used to obtain the fusion coefficients of CNN and LSTM models, that is the regression coefficient of LR, and then calculated the optimal threshold with the fused model on the verification set. In addition, in terms of model selection, lightgbm and xgboost were selected to train the model on the training set for false news, and predicted the news text on the testing set. The results of three experiments show that the effect of using xgboost model is the best, and the F1 score obtained in the experiment is the highest.
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Saeed, Ramsha, Hammad Afzal, Haider Abbas, and Maheen Fatima. "Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3461614.
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Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also resulted in the spread of fake news. Fake news might be spread by a group of people or organizations to serve ulterior motives such as political or financial gains or to damage a country’s public image. Given the importance of timely detection of fake news, the research area has intrigued researchers from all over the world. Most of the work for detecting fake news focuses on the English language. However, automated detection of fake news is important irrespective of the language used for spreading false information. Recognizing the importance of boosting research on fake news detection for low resource languages, this work proposes a novel semantically enriched technique to effectively detect fake news in Urdu—a low resource language. A model based on deep contextual semantics learned from the convolutional neural network is proposed. The features learned from the convolutional neural network are combined with other n-gram-based features and are fed to a conventional majority voting ensemble classifier fitted with three base learners: Adaptive Boosting, Gradient Boosting, and Multi-Layer Perceptron. Experiments are performed with different models, and results show that enriching the traditional ensemble learner with deep contextual semantics along with other standard features shows the best results and outperforms the state-of-the-art Urdu fake news detection model.
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Huang, Qiaoyi. "Detecting fake news with deep neural networks." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 457–63. http://dx.doi.org/10.54254/2755-2721/5/20230619.
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The raise of social media and the easy access to obtain and spread news has increased the probability of various news dissemination. However, an undetermined factor in usage scenario is the authenticity of spreading news, which has posed detrimental effects on individuals and society in current scenario. It is imperative to develop proper mechanism for fake news detection. These years, with the sharp development of deep learning, referring to the method of training multi-layer artificial neural networks, some researchers have utilized deep learning in fake news detection and classification. Although many studies have achieved a preeminent level and high accuracy, scarcity of review work regarding fake news detection with deep learning is still an important problem. This work urges to derive a comprehensive review of fake news detection and classification techniques for further study in the aspect, and mechanism in various studies for sake of raising accuracy. This work also figure out some possible challenges with existing limitation based on previous research.
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Fang, Baozhi, and Haotian Zhou. "Fake news text detection based on convolutional neural network." Applied and Computational Engineering 41, no. 1 (February 22, 2024): 202–9. http://dx.doi.org/10.54254/2755-2721/41/20230744.
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The swift evolution of mobile devices and multimedia technology has made the Internet one of the primary means of learning new information today. However, the huge amount of news information is often mixed with erroneous fake news, which can cause bad news events to spread and trigger people's bad emotions, putting the healthy development of society and economy at risk. Addressing the real-world application problem of swiftly and accurately detecting fake news is imperative. To mitigate the aforementioned challenges, we propose a method that uses deep learning to detect fake news and validate it through empirical studies. We begin by collecting a sizeable fake news dataset from domestic social media platforms and use a pre-trained deep learning model to extract textual features. Furthermore, we amalgamate convolutional neural networks and deep learning models to effectively glean and encompass the patterns and attributes of disinformation through an analysis of the text's semantic and structural characteristics. Finally, we experimentally evaluate the effectiveness of the method. The experimental findings demonstrate that the suggested approach exhibits commendable performance in the task of detecting fake news, effectively discerning between authentic and fabricated information. Our deep learning-based approach proves to be both efficient and highly impactful in addressing the issue of fake news within the realm of social media.
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Berrondo-Otermin, Maialen, and Antonio Sarasa-Cabezuelo. "Application of Artificial Intelligence Techniques to Detect Fake News: A Review." Electronics 12, no. 24 (December 18, 2023): 5041. http://dx.doi.org/10.3390/electronics12245041.
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With the rapid growth of social media platforms and online news consumption, the proliferation of fake news has emerged as a pressing concern. Detecting and combating fake news has become crucial in ensuring the accuracy and reliability of information disseminated through social media. Machine learning plays a crucial role in fake news detection due to its ability to analyze large amounts of data and identify patterns and trends that are indicative of misinformation. Fake news detection involves analyzing various types of data, such as textual or media content, social context, and network structure. Machine learning techniques enable automated and scalable detection of fake news, which is essential given the vast volume of information shared on social media platforms. Overall, machine learning provides a powerful tool for detecting and preventing the spread of fake news on social media. This review article provides an extensive analysis of recent advancements in fake news detection. The chosen articles cover a wide range of approaches, including data mining, deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based techniques.
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Ramya, S. P., and R. Eswari. "Attention-Based Deep Learning Models for Detection of Fake News in Social Networks." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–25. http://dx.doi.org/10.4018/ijcini.295809.
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Automatic fake news detection is a challenging problem in deception detection. While evaluating the performance of deep learning-based models, if all the models are giving higher accuracy on a test dataset, it will make it harder to validate the performance of the deep learning models under consideration. So, we will need a complex problem to validate the performance of a deep learning model. LIAR is one such complex, much resent, labeled benchmark dataset which is publicly available for doing research on fake news detection to model statistical and machine learning approaches to combating fake news. In this work, a novel fake news detection system is implemented using Deep Neural Network models such as CNN, LSTM, BiLSTM, and the performance of their attention mechanism is evaluated by analyzing their performance in terms of Accuracy, Precision, Recall, and F1-score with training, validation and test datasets of LIAR.
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Ghimire, Niroj, and Surendra Shrestha. "Fake News Stance Detection using Deep Neural Network." Journal of Lumbini Engineering College 4, no. 1 (December 7, 2022): 49–53. http://dx.doi.org/10.3126/lecj.v4i1.49366.
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With the advancement of technology, fake news is more widely exposed to users. Fake news may be found on the Internet, news sources and social media platforms. The spread of the fake news has harmed both individuals and society. The way to observe fake news using the stance detection technique is the focus of this paper. Given a set of news body and headline pairs, stance detection is the task of automatic detection of relationships among pieces of text. Pre-trained GloVe word embedding is used for the word to vector representation as it can capture the inter-word semantic information. The LSTM neural network had been shown efficient in deep learning applications because it can capture sequential information of input data. In this paper, it is found that the LSTM-based encoding decoding model using pre-trained GloVe word embedding achieved 93.69% accuracy on the FNC-1 dataset.
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Deng, Liwei, Hongfei Suo, and Dongjie Li. "Deepfake Video Detection Based on EfficientNet-V2 Network." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–13. http://dx.doi.org/10.1155/2022/3441549.
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As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and fake videos that seriously endanger the stability of the country and society. Examples include faking politicians to make inappropriate statements, using face-swapping technology to spread false information, and creating fake videos to obtain money. In view of this social problem, based on the original fake face detection system, this paper proposes using a new network of EfficientNet-V2 to distinguish the authenticity of pictures and videos. Moreover, our method was used to deal with two current mainstream large-scale fake face datasets, and EfficientNet-V2 highlighted the superior performance of the new network by comparing the existing detection network with the actual training and testing results. Finally, based on improving the accuracy of the detection system in distinguishing real and fake faces, the actual pictures and videos are detected, and an excellent visualization effect is achieved.
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Alyoubi, Shatha, Manal Kalkatawi, and Felwa Abukhodair. "The Detection of Fake News in Arabic Tweets Using Deep Learning." Applied Sciences 13, no. 14 (July 14, 2023): 8209. http://dx.doi.org/10.3390/app13148209.
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Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, fake news negatively impacts various aspects of life (economical, social, and political). Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, individuals are reading and sharing regardless of the truth behind the news. Identifying fake news manually on these open platforms would be challenging as they allow anyone to build networks and publish the news in real time. Therefore, creating an automatic system for recognizing news credibility on social networks relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown promising results in recognizing fake news written in English. Limited work has been conducted in the area of news credibility recognition for the Arabic language. This work proposes a deep learning-based model to detect fake news on Twitter. The proposed model utilizes the news content and social context of the user who participated in the news dissemination. In seeking an effective detection model for fake news, we performed extensive experiments using two deep learning algorithms with varying word embedding models. The experiments were evaluated using a self-created dataset. The experimental results revealed that the MARBERT with the convolutional neural network (CNN) model scores a superior performance in terms of accuracy and an F1-score of 0.956. This finding proves that the proposed model accurately detects fake news in Arabic Tweets relating to various topics.