However, most real-world problems can't be addressed by image classification only. For these use-cases, the weapon of choice is called image detection. Recently, this field has seen multiple dramatic improvements. Computers now have the ability not only to tag images but also to detect and localize items in those images. The latter now has the speed to allow real-time detection while slightly improving the accuracy of the former.
With increasing online fraud and identity theft each day, service providers need a way to ensure that their services cannot be compromised. Anti-spoofing liveness detection is required especially in unsupervised authentication situations. Biometric authentication systems need to prevent sophisticated spoofing challenges from replay attacks and determine the user's presence. Thus, BioID's presentation attack detection (PAD) is crucial for eKYC onboarding, online login and banking transactions. BioID is a pioneer and the leading player in face liveness detection for assured user presence.
Face detection is one of the most common applications of Artificial Intelligence. From camera applications in smartphones to Facebook's tag suggestions, the use of face detection in applications is increasing every single day. Face detection is the ability of a computer program to identify and locate human faces in a digital image. With the increasing demand for face detection feature in applications, everyone is looking to use face detection in their application so that they are not left behind in the race. In this post, I will teach you how to build a face detection program for yourself in less than 3 minutes.
One of the primary issues with traditional anomaly detection approaches is their inability to handle complex, structural data. One approach to this issue involves the detection of anomalies in data that is represented as a graph. The advantage of graph-based anomaly detection is that the relationships between elements can be analyzed, as opposed to just the data values themselves, for structural oddities in what could be a complex, rich set of information. However, until now, attempts at applying graph-based approaches to anomaly detection have encountered two issues: (1) Numeric values found in the data are not incorporated into the analysis of the structure, which could augment and improve the discovery of anomalies; and (2) The anomalous substructure may not be a deviation of the most prevalent pattern, but deviates from only one of many normative patterns. This paper presents enhancements to existing graph-based anomaly detection techniques that address these two issues and shows experimental results validating the usefulness of these enhancements.
Microsoft is making a cloud service that uses artificial intelligence to track down bugs in software generally available, and it will begin offering a preview version of the tool for Linux users as well. Microsoft Security Risk Detection, previously known as Project Springfield, is a cloud-based tool that developers can use to look for bugs and other security vulnerabilities in the software they are preparing to release or use. The tool is designed to catch the vulnerabilities before the software goes out the door, saving companies the heartache of having to patch a bug, deal with crashes or respond to an attack after it has been released. David Molnar, the Microsoft researcher who leads the group delivering the risk detection tool, said companies have traditionally hired security experts to do this kind of work, which is called fuzz testing, if they did it at all. As the sheer volume of software that companies create and use has increased, it's gotten harder to keep up with the dizzying pace of testing so much software – but more important than ever to keep systems safe from attackers.