System and method for digital fingerprinting of media content
Assignee9219-1568 Quebec Inc.
InventorsOlivier Hillman Beauchesne, Bertrand Chapleau, Jordan Pleet, Jose Maria Mateos Perez
Life of this patent
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Abstract
Systems, methods, and computer-readable storage media for digital fingerprinting of media content. The system does this by normalizing and pre-processing the media content into a standard format, then generating fingerprints for image, audio, and/or video aspects of the media. These fingerprints are mathematical vectors generated using one or more techniques, such as perceptual hashes coupled with machine learning. Once the fingerprints for new content is generated, the system can compare those fingerprints to fingerprints of known content to determine if the new content contains prohibited content.
Claims21
-21. (canceled)
A method comprising:
- training, via at least one processor of a computer system, a convolutional neural network (CNN) model such that the CNN model can receive an image as an input and output a vector fingerprint of the image;
- receiving media comprising video;
- executing the CNN model using the video as input, resulting in a plurality of vector fingerprints corresponding to frames of the video;
- comparing, via the at least one processor, the plurality of vector fingerprints against stored video fingerprints, resulting in candidate matches within a distance threshold;
- filtering, via the at least one processor, the candidate matches based on temporal coherence with the plurality of vector fingerprints, resulting in temporal coherence filtered matches;
- and
- presenting, via a display, the temporal coherence filtered matches.
A system, comprising:
- at least one processor;
- and
- a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
- training a convolutional neural network (CNN) model such that the CNN model can receive an image as an input and output a vector fingerprint of the image by repeatedly:
- receiving a training image and a non-training image;
- generating, via the at least one processor executing the CNN model on the training image, a first fingerprint;
- generating, via the at least one processor executing the CNN model on a transformed copy of the training image, a second fingerprint;
- generating, via the at least one processor executing the CNN model on a transformed copy of the non-training image, a third fingerprint;
- and
- modifying, via the at least one processor, the CNN model such that a first comparison between the first fingerprint and the second fingerprint are more similar than the first fingerprint and the third fingerprint,
- until modifications to the CNN model cease to create additional differences between the second fingerprint and the third fingerprint.
A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising:
- training a convolutional neural network (CNN) model such that the CNN model can receive an image as an input and output a vector fingerprint of the image;
- receiving media comprising video;
- executing the CNN model using the video as input, resulting in a plurality of vector fingerprints corresponding to frames of the video;
- comparing the plurality of vector fingerprints against stored video fingerprints, resulting in candidate matches within a distance threshold;
- filtering the candidate matches based on temporal coherence with the plurality of vector fingerprints, resulting in temporal coherence filtered matches;
- and
- presenting, via a display, the temporal coherence filtered matches.
Dependencies are inferred from the claim text.