The present invention relates to a method, apparatus, and system of distinguishing a human user and a simulated user. More particularly, the present invention relates to protecting networks against simulated human users via an image recognition arrangement. Aspects and/or embodiments seek to provide a method and system for verifying that a user is human, rather than a computer, in order to protect access to resources such as public facing websites.

There are provided systems and methods for injecting computing code for detection of malicious computing attacks during suspicious device behavior. A service provider, such as an electronic transaction processor for digital transactions, may detect activities of a computing device when using computing services. The service provider may determine that those activities are suspicious or high risk. In order to determine if the computing device is being used by a malicious user, such as to perform an automated computing attack against the service provider, the service provider may determine one or more probes that may be inserted to a corresponding user interface displayable by the computing device. The probe may attempt to differentiate between real human users and automated and/or malicious users. Computing code for the probe may be injected into the computing code for the user interface and may be provided when the user interface is output.

A method of verifying a user for transportation purposes is disclosed. The method may include using a communication apparatus to detect a face of the user. The method may include using the communication apparatus to instruct the user to perform a specific action, to validate that the specific action is performed by the user, to extract a frame from the specific action to use as an image, to obtain image parameters from the frame and to use the communication apparatus to send the image to a server for the server to determine whether the image is a genuine face by comparing the image parameters of the image with parameters in a database to obtain a comparison result and to use the comparison result to determine if the user should be verified.

FIG. 1

A method of using a user terminal to provide secure authenticated registration between a user and a third party, the method comprising: reading a chip to receive chip data stored on the chip (S408); transmitting the chip data, via a network interface of the user terminal, over a network to an authentication server associated with said third party (S410) and in response receiving via said network interface a notification message from the authentication server (S412); determining whether the identification document is authentic based on the notification message from the authentication server (S414), wherein in response to determining that the identification document is authentic, the method further comprising: outputting, via at least one output device of the user terminal, an authentication challenge to the user (S418); receiving image data as a response to the authentication challenge (S420); and transmitting the image data, via the network interface, over the network to the authentication server for authenticating the user with the third party (S422).

A method, apparatus and computer program product for using a volumetric CAPTCHA display to verify that a human is present at a computer. Responsive to a request for a computer resource, a volumetric CAPTCHA is displayed in a user interface at the computer. The volumetric CAPTCHA has a first three dimensional (3D) feature and a second 3D feature. The user is prompted to answer a question about the first 3D feature of the volumetric CAPTCHA display. The received user response to the question is evaluated for correctness in describing the first 3D feature of the volumetric CAPTCHA. In response to the received user response being correct, the user is allowed access to the computer resource. The first 3D feature and the second 3D feature have a relationship with each other in the volumetric CAPTCHA.

Provided herein is are computer-implemented methods, computer-implemented systems, and non-transitory computer-readable storage media for registering a user's eye gaze profile via a mobile device having a screen and a front facing camera. Also provided herein is are computer-implemented methods, computer-implemented systems, and non-transitory computer-readable storage media for determining a spoofing attempt by a user during a request to access a resource via a mobile device.

A set of distance measurable encrypted feature vectors can be derived from any biometric data and/or physical or logical user behavioral data, and then using an associated deep neural network (“DNN”) on the output (i.e., biometric feature vector and/or behavioral feature vectors, etc.) an authentication system can determine matches or execute searches on encrypted data. Behavioral or biometric encrypted feature vectors can be stored and/or used in conjunction with respective classifications, or in subsequent comparisons without fear of compromising the original data. In various embodiments, the original behavioral and/or biometric data is discarded responsive to generating the encrypted vectors. In another embodiment, distance measurable or homomorphic encryption enables computations and comparisons on cypher-text without decryption of the encrypted feature vectors. Security of such privacy enabled embeddings can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted credential has not been spoofed or faked.

Example systems and methods for biometric authentication that can bridge fuzzy extractors with deep learning and achieve the goals of preserving privacy and providing recoverability from zero are disclosed. Embeddings comprising a face or speaker embedding in a non-Hamming distance space can be processed to create a personal reliable bit map and a reliable locality-sensitive hash (LSH) for mapping the non-Hamming distance space to a Hamming distance space. A fuzzy extractor can be applied to create metadata that can be stored on a computing device. A secret can be recovered from the metadata and can be used for identification.

Generating verification codes includes selecting at least two verification code generators from a verification code generator set comprising a plurality of verification code generators to compose a current use set, executing each verification code generator in the current use set to obtain corresponding partial verification codes, composing a current verification code from the partial verification codes, outputting the current verification code to a user, receiving a user response that is made in response to the current verification code, and comparing the current verification code and the user response to determine whether the user is verified.

Methods, apparatus, systems, and storage medium for a face liveness detection are provided. The method includes obtaining an image comprising a face of an object; extracting an image feature of the image through an encryption network in a joint model for encryption and detection; performing image reconstruction based on the image feature to obtain an encrypted image corresponding to the image, the encrypted image being different in image content from the image; transmitting the encrypted image to a liveness detection server, wherein the liveness detection server is configured to perform liveness detection on the encrypted image through a detection network in the joint model for encryption and detection to obtain a liveness detection result of the object in the image; and receiving the liveness detection result of the object in the image from the liveness detection server.