CORTEXA
← Browse
crossrefElectronics2023-12-25Cited by 90

A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods

Gourav Gupta, Kiran Raja, Manish Gupta, Tony Jan, Scott Thompson Whiteside, Mukesh Prasad

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 information on available benchmark datasets in DeepFake detection research. This review paper addressed the 67 primary papers that were published between 2015 and 2023 in DeepFake detection, including 55 research papers in image and video DeepFake detection methodologies and 15 research papers on identifying and verifying speaker authentication. This paper offers lucrative information on DeepFake detection research and offers a unique review analysis of advanced machine learning and modality fusion that sets it apart from other review papers. This paper further offers informed guidelines for future work in DeepFake detection utilizing advanced state-of-the-art machine learning and information fusion models that should support further advancement in DeepFake detection for a sustainable and safer digital future.

View free PDFSource page

Related papers

crossrefElectronics2022-05-19Cited by 13

Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home

George Vardakis, George Tsamis, Eleftheria Koutsaki, Kondylakis Haridimos, Nikos Papadakis

Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity tha…

View free PDFSource page
crossrefElectronics2026-04-28

Enhancing Intrusion Detection Systems Using Machine Learning and Advanced Feature Selection Methods

Ahmed Abu-Khadrah, Shaima AlKhudair, Mohammad R. Hassan, Ali Mohd Ali, Tareq A. Alawneh, Emad Alnawafa, et al.

Machine learning helps intrusion detection systems learn new assaults quickly. These systems train on a dataset with several threats and may identify odd behavior. This research detects intrusion using Random Forest, KNN, and Gaussian Naive Bayes. We run the model on a comprehens…

View free PDFSource page
crossrefElectronics2022-08-18Cited by 39

Memory Forensics-Based Malware Detection Using Computer Vision and Machine Learning

Syed Shakir Hameed Shah, Abd Rahim Ahmad, Norziana Jamil, Atta ur Rehman Khan

Malware has recently grown exponentially in recent years and poses a serious threat to individual users, corporations, banks, and government agencies. This can be seen from the growth of Advanced Persistent Threats (APTs) that make use of advance and sophisticated malware. With t…

View free PDFSource page
crossrefElectronics2024-04-26Cited by 74

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Tanjim Mahmud, Michal Ptaszynski, Fumito Masui

Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian langu…

View free PDFSource page
crossrefElectronics2023-10-17Cited by 7

Network Intrusion Detection Based on Amino Acid Sequence Structure Using Machine Learning

Thaer AL Ibaisi, Stefan Kuhn, Mustafa Kaiiali, Muhammad Kazim

The detection of intrusions in computer networks, known as Network-Intrusion-Detection Systems (NIDSs), is a critical field in network security. Researchers have explored various methods to design NIDSs with improved accuracy, prevention measures, and faster anomaly identificatio…

View free PDFSource page
crossrefElectronics2026-01-22Cited by 3

Advanced Fault Detection and Diagnosis Exploiting Machine Learning and Artificial Intelligence for Engineering Applications

Davide Paolini, Pierpaolo Dini, Abdussalam Elhanashi, Sergio Saponara

Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensi…

View free PDFSource page