# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Semantic Features  
Fake news is intentionally created to manipulate the individual weaknesses of human beings. Thus, faux images are dramatically exaggerated to incite anger or hate reactions in public, which leads to further dissemination of fake news. These deliberative manipulations have some distinct cues at the semantic level in images that contrast with real news.  
[PERSON] shares fake news spreaders' emotional and behavioral studies [73]. The research shared real-time examples of herding behavior where the fake news spread is amplified by people sharing the same views or interests. It is also known as the echo chamber effect. It is noticed that people have a perception that complete falsification is not factual, but sensationalist or partisan news does contain some aspects of truth. Faux news generators utilize these behavior patterns to spread their intent fast into society. Based on the above observation, [PERSON] et al. [74] proposed a model built on psychologically triggered visual patterns in fake images. They modeled a domain-transferred deep convolutional neural network with weighted instances and trained over 40k images. Some interesting visual semantic patterns were observed from the results: fake images tend to be more eye-catching, event-centric, disturbing, and low-quality than real ones. These cues confirm observations by [PERSON]. [PERSON] et al. [75] used psychological and social theories in combination with data mining. The study shows that fake news detection techniques are primarily based on text or social context content. The social context cues over images can play a significant role in detecting false news. [PERSON] et al. [76] suggested utilizing the semantic and stylistic elements of the suspicious account to identify the bogus credibility of the news created from these accounts.  
In contrast to the above methods, [PERSON] et al. [38] proposed a self-supervised method that uses an algorithm based on the picture's EXIF metadata as a supervisory signal. These signals are trained in a ResNet50 framework to decide whether a photo is self-consistent. If the photo is self-consistent/untampered, then its constituents should be generated by a single imaging pipeline. This approach has a few limitations, as it depends on the EXIF metadata information of the device. The model is not well-suited for detecting minor splicing over an image. It also becomes confused with underexposed and overexposed regions of the picture. It does not work well with the copy-and-move tampering method, as the manipulation is from the same image. On the other hand, [PERSON] et al. [77] proposed a method using photo-response non-uniformity (PRNU). Modern PRNU-based forensics techniques often depend on Markov random field modeling with multi-scale trace analysis and result fusion.  
Issues with semantic feature detection techniques are their limitation to the semantic features in an image based on behavior or psychological patterns. Each of these patterns, despite knowing, is subjective, and they are updated with evolving technology and public behavior. They will often require domain expertise as the model interpretability is complex. Alone with semantic features, they will need other elements to derive more successful results.