# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 2 Fake Image Detection Process  
Detection of fake news over digital media has long been challenging. Multiple research works using different techniques are used to detect fake news. Figure 5 illustrates the various methods employed in fake news detection. It can be detected using social context-based, content-based, and user profile-based strategies. Text-based linguistic, image-based, and text style-based approaches are used in content-based. It can be observed from the taxonomy of Figure 5 that even in image-based detection, there are multiple methods. Therefore, this survey's scope is limited to image-based detection methods. Digital signatures and digital watermarking fall under active methods. However, these active methods are not feasible, with many images added over the internet. Passive methods can be categorized into two broad categories. One requires a handcrafted feature set of images, while others are based on neural networks that learn the feature set.  
Conventional domain-specific image forensic techniques are used in a handcrafted feature set approach. These forensic techniques are now combined with machine learning for better optimization. Other methods can be used for the semantic and statistical features of the image. Web-Retrieval is another popular method for searching and identifying tampered images. On the other hand, in the neural network-based approach, convolutional neural networks (CNN) are noticed to be very useful in learning the intrinsic features of the image.  
Figure 5: Fake News Detection Taxonomy—Image-Based approaches.  
the manipulated image. Much research was conducted through CNN. The multi-modal approach is currently being applied, i.e., combining images with text, images with social context, etc. These multi-modal approaches also use the same neural networks as CNN. The paper by [PERSON] et al. [15] discusses various techniques.  
Fake image detection is a classification problem. The final output is identifying whether the image is fake or not. The process starts with gathering various types of tampered images manipulated using single or multiple alterations and then, after processing, classifying them as real or false. The fake image detection process at a high level comprises handcrafted feature sets and self-learning neural networks. Figure 6a exemplifies a fake image detection workflow using handcrafted features. Initially, a set of tampered images is collected. Then, each image may undergo pre-processing activity, like gray scaling and cropping. In the feature extraction phase, various image features are extracted relating to the image. These features can be device-specific, image-intrinsic, or semantic/statistics characteristics of an image. Forensic methods use handcrafted intrinsic features of images, while other methods use other characteristics. Feature preprocessing may or may not be applied to reduce features to achieve computational efficiency. Figure 6b illustrates the process used by neural networks, which learn the fake image's hidden features. Ultimately, both processes have a classifier applied to mark them as real or fake based on the learnings. Sometimes, based on the detection method capability, image post-processing is also performed. In post-processing, the tampered regions in an image are identified. Forensic feature techniques are proficient in localizing the manipulated areas in a fake image.