# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 1 Introduction
### Contribution and Organization  
This survey research paper contributes significantly to the field of fake image detection, offering valuable insights and uniqueness compared to other surveys. This paper focuses  
Figure 4: President [PERSON] reading a book, upturned [10].  
on detecting fake images shared over social media platforms, a crucial aspect of identifying fake news on digital platforms. Other surveys have focused on general image forgery, not on the most impacted area of image forgery--social media platforms. It comprehensively reviews various techniques, from traditional forensics to cutting-edge deep learning approaches, making it distinct and relevant for further research.  
The unique contributions of this paper include:  
* Comprehensive Coverage: This paper thoroughly examines the fake image detection process, leaving no stone unturned. It explores image tampering techniques, including Generative Adversarial Networks (GANs). It covers various detection methods, encompassing handcrafted forensic features, semantic features, statistical features, web retrievals, neural networks, and multi-modal approaches;
* Performance Comparison: It goes beyond describing these methods by summarizing and comparing their results within each detection category. This performance evaluation aids researchers in selecting the most suitable approach for their specific needs;
* Deep Learning Emphasis: This paper underscores the superiority of deep learning methods for detecting fake images over social media platforms, backed by evidence and comparative analysis. This emphasis provides clear guidance to researchers and practitioners;
* Challenges and Future Scope: It does not shy away from highlighting the current challenges and limitations in the field, shedding light on areas where further research is needed. This forward-looking perspective enhances its value for the research community;
* Dataset and Evaluation Parameters: This paper also provides valuable information on fake image datasets and evaluation parameters, facilitating the replication of experiments and benchmarking new detection methods.  
This survey paper is a comprehensive and up-to-date resource for researchers and practitioners interested in fake image detection. Its emphasis on deep learning, comprehensive coverage, performance comparison, and forward-looking perspective make it a unique and valuable contribution to the field, guiding future research efforts and advancements in this critical area. The rest of this paper is organized as follows: Section 2 describes fake image detection processes and their methods; Section 3 provides a brief description of various image tampering techniques; Section 4 details the research work performed to detect fake images using a handcrafted feature set. Their comparisons and issues are also discussed; Section 5 presents detection methods using neural networks. A comparison among multiple methods is also presented; Section 6 targets evaluation parameters and datasets, respectively; Section 7 briefs the challenges and limitations of current work and guides toward future work. The conclusion is provided in Section 8.