Iris liveness detection is an important module in an iris recognition system to reduce the risks of being spoofed by fake iris patterns at the sensor input. A general framework is proposed to detect multiple types of fake iris images based on texture analysis. A novel iris pattern representation method namely hierarchical visual codebook (HVC) is proposed to encode the distinctive and robust te...

Recent research works have revealed that it is not difficult to spoof an automated iris recognition system using fake iris such as contact lens and paper print etc. Therefore, it is very important to detect fake iris as much as possible. In this paper, we propose a new fake iris detection method based on wavelet packet transform. First, wavelet packet decomposition is used to extract the featur...

Fake review detection has been studied by researchers for several years. However, so far all reported studies are based on English reviews. This paper reports a study of detecting fake reviews in Chinese. Our review dataset is from the Chinese review hosting site Dianping, which has built a fake review detection system. They are confident that their algorithm has a very high precision, but they...

The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related ...

Objective: Fake honeys from the Ecuadorian market were collected and analyzed with the quality indicators of the Venezuelan honey norm and a simple test. Method: Ash, free acidity, moisture, reducing sugars, apparent sucrose, qualitative diastase activity, qualitative hydroxymethylfurfural, and a simple test based on the number of phases formed after shaking honey water dilutions with diethyl e...

Users increasingly rely on crowdsourced information, such as reviews on Yelp and Amazon, and liked posts and ads on Facebook. This has led to a market for blackhat promotion techniques via fake (e.g., Sybil) and compromised accounts, and collusion networks. Existing approaches to detect such behavior relies mostly on supervised (or semi-supervised) learning over known (or hypothesized) attacks....

Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, including 1) they occur in short bursts of time; 2) fraudulent user accounts have skewed rating distributions. However, these may both be true in any given dataset. Henc...