# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Image Retrieval/Web Search  
Image retrieval or reverse image search is often the most common activity a user performs when it senses any tampering and looks to verify the integrity of the image. Image search engines are now advanced and mostly retrieve other sources with similar images. Commonly used search engines specially designed for these reverse searches are Tineye, Picsearch, Google image reverse search, Yander, and Yahoo image search.  
[PERSON] et al. [78] analyzed the survey of over 200 people and tried to predict the user intent in searching images over the web. The study shows that user behavior in searching the web correlates with the intent of his search. Patterns like dwell time, mouse hover, mouse click, and query reformulation can predict the user's intentions. Later, based on user intention, the search engine can provide the exact images the user is searching. Taking learning from the above survey study, next year, [PERSON] et al. [79] again proposed a grid-based evaluation matrix implemented in alternative to Discounted Cumulative Gain(DCG) or Rank-Biased Precision (RBP), which are traditional list-based metrics. This time, the suggestion was proposed after studying user patterns like middle bias, slower delay, and row skipping. However, this study did not include appearance bias and was conducted on a few people.  
[PERSON] and [PERSON] proposed an interactive information retrieval process by taking text with visual images over social media platforms. A user study was conducted, and the ImgSEE image database [80] was created, which was designed based on [PERSON]'s three-stage model of information seeking. The technique is collaborative with exploratory search and sense-making processes. These are useful for an image-search activity with less information, and the user may want to verify what they seek. The study also validated the efficacy of this technique by comparing it with a grid-based search method using candidates' views on usefulness, ease, and satisfaction. At the same time, considering text over an image, a novel technique was employed by [PERSON] et al., which authenticates the accuracy of text existing over an image by searching for it on the internet and introduces the Reality parameter [81]. The Reality parameter (Rp) is calculated by checking the text's reliability from the top Google search results. The event is marked as real or fake based on the Rp value. However, the technique has limitations when correct text is not extracted from the image using an OCR. If the news depends on geography, it does not gather enough credibility to appear in top searches and will be wrongly termed fake. Issues with web retrieval methods are as follows:  
* Not all images can be searched over the reverse web search;
* Images/news not gathering enough highlights will not be ranked in the initial few pages;
* Reverse image searches will also bring images from fake websites wherein such fake images are spread;
* It requires time for the fake image to spread; searching before it becomes viral will not fetch any relevant information;
* Searching fake videos over the web is a tight task and requires effort and time.