Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/123188
Title: A survey of detection and mitigation for fake images on social media platforms
Authors: Sharma, Dilip Kumar
Singh, Bhuvanesh
Agarwal, Saurabh
Garg, Lalit
Kim, Cheonshik
Jung, Ki-Hyun
Keywords: Deepfakes -- Detection
Deep learning (Machine learning)
Fake news -- Detection
Disinformation -- Prevention
Digital forensic science
Issue Date: 2023
Publisher: MDPI AG
Citation: Sharma, D. K., Singh, B., Agarwal, S., Garg, L., Kim, C., & Jung, K. H. (2023). A survey of detection and mitigation for fake images on social media platformss. Applied Sciences, 13(19), 10980.
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.
URI: 10.3390/app131910980
https://www.um.edu.mt/library/oar/handle/123456789/123188
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