A Review of Face Detection Anti Spoofing Techniques on Varied Data Sets

In the recent scenario, and also during the pandemic situation everything is processed as well as transferred digitally. Nowadays from kids to an Adult, every age group relies on digital platform which may result in cybercrime. Nowadays cybercrimes are on its peak. E.g. user’s photo can simply be found on social media, these photos can be spoofed by facial recognition software (FRS). This digital face identity theft can be used to attempt varied activities related to money which can lead to banking fraud. Spoofing is a type of scam in which criminals attempt to obtain someone’s personal information by pretending to be a legitimate business, a neighbor, or some other innocent party. To intercept these problems of recognizing real faces against fake faces, various researchers determine Face Anti-Spoofing techniques on varied data sets. Existing research still faces difficulties to solve spoofing attacks in the real world, as datasets are limited in both quantity and quality. The key aim of this paper is to contribute a detail study of Face Anti-spoofing techniques and evaluation of varied Datasets. Finally, we achieved from the study that many researchers have found truthful methods which solve spoofing threats. But, existing work requires a more proficient face anti-spoofing algorithm by which cyber crimes can be reduced.