Abstract: A system and method voice driven animation of an object in an image by sampling an input video, depicting a puppet object, to obtain an image, receiving audio data, extracting voice related features from the audio data, producing an expression representation based on the voice related features, wherein the expression representation is related to a region of interest, obtaining from the image, auxiliary data related to the image and generating a target image based on the expression representation and the auxiliary data.

Abstract: A system and method of animating an image of an object may include: receiving a first image, depicting a “puppet” object; sampling an input video, depicting a second, “driver” object, to obtain at least one second image; obtaining, by a first machine-learning (ML) model, a first identity-invariant feature of the puppet object, from the first image; obtaining at least one second identity-invariant feature of the driver object, from the respective at least one second image; calculating, by a second ML model, a transformation function, based on the first identity-invariant feature and the at least one second identity-invariant feature; applying the calculated transformation function on the first image, to produce one or more third images, depicting a target object, including at least one identity-invariant feature of the driver object; and appending the one or more third images to produce an output video depicting animation of the puppet object.

Abstract: A system and method of animating an image of an object by extracting a plurality of three dimensional (3D) features from a first image, depicting a puppet object, and from the second image of a driver object, obtaining, a value of a first 3D identity-invariant feature of the puppet object, and a value of a second 3D identity-invariant feature, from the second image, calculating and applying, a mixing function, on the plurality of 3D features, generating a rendered image, based on the plurality of 3D features, the first 3D identity-invariant feature and the second 3D identity-invariant feature, and generating an output image based on the rendered image, the plurality of 3D features, the first image and the second image, wherein the output image depicting a target object comprising at least one 3D identity-invariant feature of the driver object.

Abstract: To consistently anonymize video, an identity-preserving encoder may be trained with a decoder to preserve identity information unique to an individual in an encoded space. The identity-preserving encoder may encode source video images of a source identity to generate identity-preserving encoded data representing the source identity. A de-identification engine may generate de-identifying encoded data representing a different target identity. A transformation may be generated based on a difference between the identity-preserving and de-identifying encoded data, representing swapping the source identity with the target identity. The identity may be consistently swapped from the source to target in the encoded space by applying the same transformation to the identity-preserving encoded data for the source images.

Abstract: System and method of performing identity authentication based on de-identified data, with receiving of at least one data item from a user, creating at least one de-identified data item and a corresponding authentication key, based on the received at least one data item, storing the at least one de-identified data item in a database, receiving at least one new data item for authentication, retrieving the authentication key corresponding to the user, generating at least one new de-identified data item based on the received at least one new data item, and comparing the at least one new de-identified data item with the stored at least one de-identified data item to authenticate the identity of the user.

Abstract: A system and a method of prevention and/or detection of adversarial attacks against a classifier may include for example: using a classifier adapted to classify data elements of a specific category; receiving a first data element; applying the classifier on the first data element, to produce a first classification of the data element; using an autoencoder to generate a reconstructed, second data element based on the first data element; applying the classifier on the second data element, to produce a second classification of the data element; and analyzing the first and second classifications to detect and/or prevent an adversarial attack on the classifier.

Abstract: System and method for training a human perception predictor to determine level of perceived similarity between data samples, the method including: receiving at least one media file, determining at least one identification region for each media file, applying at least one transformation on each identification region for each media file until at least one modified media file is created, receiving input regarding similarity between each modified media file and the corresponding received media file, and training a machine learning model with an objective function configured to predict similarity between media files by a human observer in accordance with the received input.