A Comprehensive Review of Machine Learning Techniques for DeepFake Detection
Md. Humayun Kabira✉, Abdullah Al Nomanb, Abdullah Al Afiqb, Hassan Jakib, Mohammad Nadib Hasanc, Ahmadd, Md. Saiful Islama, Hasibul Hasanb, Imroj Ahasan Mojumderb
- aDepartment of Electronics and Telecommunication Engineering , Chittagong University of Engineering & Technology , Chattogram , 4349 , Bangladesh , cuet.ac.bd
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
DeepFake technology is rapidly advancing, fundamentally changing how digital media is created and consumed. This evolution brings both exciting opportunities, such as enhanced entertainment and communication, and serious challenges, including misinformation, fraud, and threats to privacy and security. Using advanced AI methods such as deep learning and generative models, DeepFakes can create highly realistic but fake images, text, audio, and videos that are hard to identify. These synthetic media are often hard to distinguish from real content, which raises concerns about their misuse. This research presents a comprehensive review of the latest DeepFake detection methods across different types of media, including images, audio, video, and text. We investigate the application of deep learning models for detecting fraudulent information, including transformers, hybrid frameworks, convolutional neural networks (CNNs), and generative adversarial networks (GANs). By analyzing a wide range of studies, we compare these methods based on their accuracy, speed, and ability to perform well on various datasets such as FaceForensics++, DFDC, and DeepFakeTIMIT. One significant issue we identify is the lack of diversity in current datasets, which often leads to biased detection results and weak performance when models encounter new or real‐world DeepFakes. The present research emphasizes the need to develop better dataset standards that respect ethical and privacy concerns. Improving these areas will improve the reliability and generalization of detection systems. Our findings emphasize the need to develop adaptive detection models, privacy‐preserving techniques, and ethical frameworks to address emerging threats. The overall goal of this study is to give practitioners and scholars a comprehensive grasp of the state of DeepFake detection today. By identifying key challenges and suggesting promising future directions, we hope to support efforts to build safer digital environments where trust and authenticity are preserved.
TL;DRThis research presents a comprehensive review of the latest DeepFake detection methods across different types of media, including images, audio, video, and text, and investigates the application of deep learning models for detecting fraudulent information, including transformers, hybrid frameworks, convolutional neural networks (CNNs), and generative adversarial networks (GANs). (Semantic Scholar)