# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 8 Conclusions  
This research paper on recognizing and reducing fake Images on social media platforms delves into critical issues in this domain. Fake images on social media have become a pressing concern, prompting social media platforms to combat this issue. This survey assesses various techniques for identifying fake pictures on these platforms. This paper discusses different tampering methods for creating fake images, from conventional to recent Generative Adversarial Network (GAN)-generated manipulations. It highlights the fake image detection process, reviewing multiple detection methods, including those using handcrafted features and neural network-based approaches. Performance comparisons for these methods and their respective advantages and limitations are presented. Forensic techniques are noted for accuracy but are less efficient in detecting fake images subjected to multiple manipulations. While they can localize tampered areas, they may not address distinct alterations common in fake photos shared on social media. Semantic and statistical features also have their limitations. This paper emphasizes recent neural network-based detection methods based on Convolutional Neural Network (CNN) architectures. CNN models prove highly resistant to multiple manipulations, effectively identifying fake photos without pre- or post-processing limitations. CNNs are also efficient in detecting deepfake images and videos. However, the vision transformer has shown better results on GAN-generated deepfakes. Multi-modal approaches are discussed, combining visual and text content results, providing enhanced authenticity prediction. Evaluation parameters and dataset information are shared. The research explores various methods, from traditional forensics to deep learning, concluding that deep learning methods outperform others in identifying fake images on social media. However, they depend on extensive labeled datasets, which can be challenging to obtain for fake images. Cross-platform and interpretability issues are highlighted, with multi-modal approaches offering improved accuracy.  
This paper calls for further research in multi-modal combinations and the creation of substantial, real-time labeled datasets to support the development of more efficient, generic models. In summary, this study underscores the significance of deep learning in fake image detection while acknowledging data availability and interpretability challenges, advocating for a comprehensive multi-modal approach and more extensive datasets to advance research in this critical area.  
Conceptualization, D.K.S. and B.S.; validation, D.K.S., B.S., S.A., L.G., C.K. and K.-H.J.; formal analysis, D.K.S., B.S., S.A., L.G., C.K. and K.-H.J.; writing--original draft preparation, D.K.S., B.S. and S.A.; writing--review and editing D.K.S., B.S., S.A., L.G. and K.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049788).  
Not applicable.  
The datasets used in this paper are publicly available, and their links are provided in the reference section.  
We thank the anonymous reviewers for their valuable suggestions that improved the quality of this article.  
The authors declare no conflict of interest.