Abstract: A method for classifying a digital asset, comprising: retrieving from a repository, according to an initial identifier, a digital asset; computing at least one asset score, each indicative of a degree of confidence that the digital asset has a respective offense classification selected from a plurality of offense classifications; subject to the at least one asset score being less than at least one threshold score: adding to a list of identifiers at least one identifier extracted from the digital asset; and in each of at least one iteration: retrieving from the repository at least one other digital asset, according to at least one selected identifier selected from the list of identifiers; computing a plurality of other asset scores, each associated with one other digital asset of the at least one other digital asset and indicative of another degree of confidence that the other digital asset has another respective offense classification.

Abstract: There is provided a method of training a machine learning model, comprising: extracting faces depicted in image(s), creating an age training dataset comprising records, each including a face and a ground truth label indicating whether the face is below a legal age, training an age component on the age training dataset for generating a first outcome indicative of a target face from the target streaming being below the legal age, creating a sexuality training dataset comprising records, each including image(s) and ground truth label indicative of sexuality depicted therein, training a sexuality component on the sexuality training dataset for generating a second outcome indicative of sexuality depicted in the target streaming, defining a combination component that receives an input of a combination of the first outcome and the second outcome, and generates a third outcome indicative of child sexual abusive materials (CSAM) depicted in the target streaming.

Abstract: There is provided a method of training a machine learning model, comprising: extracting faces depicted in videos, creating an age training dataset comprising records, each including a face and a ground truth label indicating whether the face is below a legal age, training an age component on the age training dataset for generating a first outcome indicative of a target face from the target video being below the legal age, creating a sexuality training dataset comprising records each including frame(s) and ground truth label indicative of sexuality depicted therein, training a sexuality component on the sexuality training dataset for generating a second outcome indicative of sexuality depicted in target frame(s) of the target video, defining a combination component that receives an input of a combination of the first outcome and the second outcome, and generates a third outcome indicative of child sexual abusive materials (CSAM) depicted in the target frame(s).

Abstract: There is provided a method of training a machine learning model, comprising: extracting faces from first images, creating an age training dataset comprising records each including a face and a ground truth label indicating whether the face is below a legal age, training an age component on the age training dataset for generating a first outcome indicative of a target face of the target image being below the legal age, creating a sexuality training dataset comprising second records each including a second image and ground truth label indicative of sexuality, training a sexuality component on the sexuality training dataset for generating a second outcome indicative of sexuality depicted in the target image, defining a combination component that receives an input of a combination of the first outcome and the second outcome, and generates a third outcome indicative of child sexual abusive materials (CSAM) depicted in the target image.

Abstract: A method for classifying a digital asset, comprising: retrieving from a repository, according to an initial identifier, a digital asset; computing at least one asset score, each indicative of a degree of confidence that the digital asset has a respective offense classification selected from a plurality of offense classifications; subject to the at least one asset score being less than at least one threshold score: adding to a list of identifiers at least one identifier extracted from the digital asset; and in each of at least one iteration: retrieving from the repository at least one other digital asset, according to at least one selected identifier selected from the list of identifiers; computing a plurality of other asset scores, each associated with one other digital asset of the at least one other digital asset and indicative of another degree of confidence that the other digital asset has another respective offense classification.

Abstract: A system for processing digital data signals, comprising at least one hardware processor adapted for identifying an offending social interaction by: receiving at least one signal from at least one other hardware processor, where each of the at least one signal is generated according to an action of a person and is associated with at least some of a plurality of entities, each entity having a plurality of basic entity confidence values of a plurality of basic entity attributes, and a plurality of combination entity confidence values of a plurality of combination entity attributes; and for at least one entity of the plurality of entities: updating at least one basic entity confidence value thereof according to a plurality of signal attribute values computed for the at least one signal; updating at least one combination entity confidence value thereof according to a relationship tree describing a semantic relationship.

Abstract: A system for processing digital data signals, comprising at least one hardware processor adapted to identifying an offending social interaction by: in at least one of a plurality of iterations: receiving a signal from a first other hardware processor, where the signal is generated according to an action of a first person, has a plurality of signal attributes, and is associated with a plurality of entities comprising a first entity and a second entity, each entity having a plurality of entity confidence values of a plurality of entity attributes; identifying at least one correlation between the signal and at least one other signal received from at least one second other hardware processor in at least one other of the plurality of iterations, the at least one other signal generated according to at least one other action of at least one second person and associated with another plurality of entities comprising.