A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
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Published:2023-10-05
Issue:19
Volume:13
Page:10980
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Sharma Dilip Kumar1ORCID, Singh Bhuvanesh2, Agarwal Saurabh34ORCID, Garg Lalit5ORCID, Kim Cheonshik6ORCID, Jung Ki-Hyun4ORCID
Affiliation:
1. Department of Computer Engineering and Application, GLA University, Mathura 281406, India 2. Graduate Software Programs, University of St. Thomas, St. Paul, MN 55105, USA 3. Department of Computer Science and Engineering, Amity School of Engineering Technology, Amity University Uttar Pradesh, Noida 201313, India 4. Department of Software Convergence, Andong National University, Andong-si 36729, Republic of Korea 5. Computer Information Systems, Faculty of Information & Communication Technology, University of Malta, 2080 Msida, Malta 6. Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea
Abstract
Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.
Funder
National Research Foundation of Korea Ministry of Education
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference138 articles.
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