Abstract: Methods and devices for spoofing proof authentication of a user by voice to unlock a user device. According to one of its aspects, a method includes performing spoofing proof authentication of a user by voice. According to another aspect, a device is configured for spoofing proof authentication of a user by voice. Implementation of the method provides not only personalized access to the data, but also the highest level of security due to anti-spoofing.

Abstract: Methods and devices identify a speaker by extracting speech features from at least one keyword. A speaker vector is produced by feeding the extracted speech features to a pre-trained neural network. The pre-trained neural network includes a convolutional neural network. The convolutional neural network serves as a backbone and provides a backbone embedding. The pre-trained neural network also includes a neural subnetwork. The produced speaker vector is compared with at least one of registered speaker vectors corresponding to known speakers to identify the speaker.

Abstract: A method for performing a crowdsourcing task is provided. The method executed on a computing device comprises generating a crowdsourcing task to take at least one image of an original face photo; sending the generated crowdsourcing task to a crowdsourcing server to publish it thereon, the crowdsourcing server communicating with at least one user device registered in the crowdsourcing server; receiving at least one confirmation from the crowdsourcing server, each confirmation corresponding to the crowdsourcing task accepted by a particular user with a corresponding one of the registered user devices; providing an original face photo for each user corresponding to one of the received confirmations; sending, through the crowdsourcing server, the original face photo to a particular user device corresponding to the user; and receiving at least one image from the user device, the received images being taken for the original face photo in accordance with the accepted crowdsourcing task.

Abstract: The present application relates to the field of computer software technologies, and in particular, to a face spoofing attack detection method and system. The suggested method comprises capturing an image; determining a context of the captured image; highlighting a part of the captured image to generate an image with the highlighted part, wherein the highlighted part is a potential spoofing region determined based on the context of the captured image; combining the captured image with the image with the highlighted part to generate a summarized image; performing face spoofing attack detection in the summarized image, wherein only the highlighted part has priority in the face spoofing attack detection, generating a general spoofing score as a result of the face spoofing attack detection; comparing the general spoofing score result with a threshold for face liveness detection.

Abstract: Methods and devices for detecting a spoofing attack are provided. The method includes receiving a speech signal; applying a Fast Fourier Transform (FFT) to the speech signal to extract FFT features therefrom and feeding the extracted FFT features to a pre-trained deep neural network to assign a first confidence score to the speech signal; applying a Discrete cosine transform (DCT) to the speech signal to extract DCT features therefrom and feeding the extracted DCT features to a pre-trained deep neural network to assign a second confidence score to the speech signal; applying a pre-trained deep convolutional network (DCN) based on an end-to-end architecture to the speech signal to assign a third confidence score to the speech signal; computing a total confidence score based on the assigned confidence scores; and comparing the computed total confidence score to a predefined threshold to detect whether the received speech signal is spoofed.

Abstract: A user authentication method in a messaging application of an electronic device. The method comprises, if at least one text message is typed by a user in the messaging application, collecting image data relating to said user and behavioral data relating to said user, and, if at least one voice message is pronounced by said user in the messaging application, collecting image data relating to said user and voice data relating to said user.

Abstract: A method for performing a crowdsourcing task is provided. The method executed on a computing device comprises generating a crowdsourcing task to take at least one image of an original face photo; sending the generated crowdsourcing task to a crowdsourcing server to publish it thereon, the crowdsourcing server communicating with at least one user device registered in the crowdsourcing server; receiving at least one confirmation from the crowdsourcing server, each confirmation corresponding to the crowdsourcing task accepted by a particular user with a corresponding one of the registered user devices; providing an original face photo for each user corresponding to one of the received confirmations; sending, through the crowdsourcing server, the original face photo to a particular user device corresponding to the user; and receiving at least one image from the user device, the received images being taken for the original face photo in accordance with the accepted crowdsourcing task.

Abstract: Methods and devices for spoofing proof authentication of a user by voice to unlock a user device. According to one of its aspects, a method includes performing spoofing proof authentication of a user by voice. According to another aspect, a device is configured for spoofing proof authentication of a user by voice. Implementation of the method provides not only personalized access to the data, but also the highest level of security due to anti-spoofing.

Abstract: Methods and devices for detecting a spoofing attack are provided. The method includes receiving a speech signal; applying a Fast Fourier Transform (FFT) to the speech signal to extract FFT features therefrom and feeding the extracted FFT features to a pre-trained deep neural network to assign a first confidence score to the speech signal; applying a Discrete cosine transform (DCT) to the speech signal to extract DCT features therefrom and feeding the extracted DCT features to a pre-trained deep neural network to assign a second confidence score to the speech signal; applying a pre-trained deep convolutional network (DCN) based on an end-to-end architecture to the speech signal to assign a third confidence score to the speech signal; computing a total confidence score based on the assigned confidence scores; and comparing the computed total confidence score to a predefined threshold to detect whether the received speech signal is spoofed.

Abstract: The present application relates to the field of computer software technologies, and in particular, to a face spoofing attack detection method and system. The suggested method comprises capturing an image containing a face of a user; highlighting at least one part of the captured image, which is a potential spoofing region; selecting a method for user liveness detection for a specific highlighted at least one part of the captured image, which is a potential spoofing region; performing user liveness detection using the selected method and based on the highlighted at least one part of the captured image to obtain a general liveness score; and comparing the general liveness score result with a threshold for face spoofing attack detection.

Abstract: A system and method and apparatus collect behavioral biometric and facial recognition data for biometric analysis by asking a user to type text to an electronic device. Fusion of knowledge based authentication, behavioral biometrics and facial recognition provides strong authentication and liveness detection, while keeping user experience to simply typing.

Abstract: A method for user authentication based on keystroke dynamics is provided. The user authentication method includes receiving a keystroke input implemented by a user; separating a sequence of pressed keys into a sequence of bigrams having bigram names simultaneously with the user typing free text; collecting a timing information for each bigram of the sequence of bigrams; extracting a feature vector for each bigram based on the timing information; separating feature vectors into subsets according to the bigram names; estimating a GMM user model using subsets of feature vectors for each bigram; providing real time user authentication using the estimated GMM user model for each bigram and bigram features from current real time user keystroke input. The corresponding system is also provided. The GMM based analysis of the keystroke data separated by bigrams provides strong authentication using free text input, while user additional actions (to be verified) are kept at a minimum.

Abstract: A computer-implemented method for verifying identity of a speaker is proposed. A low dimensional p-vector based on a speech of the speaker is extracted from the generated high dimensional speaker model and is then compared with the stored specific speaker's p-vector obtained previously during the enrollment process. The resulting biometric score is then used to determine whether to verify the speaker, or not.