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US 2026/0099720 A1Published application
Training a content authenticity validator as a variable resolution game
AssigneeD5Ai LLC
InventorJames K. BAKER
Life of this patent
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Abstract
Computer-implemented systems and method train a generator and a discriminator, through machine learning, where the generator and discriminator are trained in an adversarial relationship using a simulated, multi-player game. The model parameters for the generator and the discriminator can be updated non-simultaneously. Also, the simulated, multi-player game may comprise a two-person, zero-sum game.
Claims36
A method of building a content authenticity validator, the method comprising:
- training, by a programmed computer system, through machine learning, a generator and a discriminator together in a multi-player, multi-round simulated game, where in each round of the multi-player, multi-round simulated game, the discriminator is trained to determine whether a selected data item, presented to the discriminator, is from the generator or from a data source that is different from the generator, wherein the training comprises:
- training, by the programmed computer system, the discriminator to perform first and second tasks, wherein:
- the first task is whether the selected data item is from the generator or from the data source;
- and
- the second task is whether the selected data item is authentic;
- training, by the programmed computer system, the generator to generate data that the discriminator incorrectly determines are not from the generator;
- and
- updating, iteratively and non-simultaneously, by the programmed computer system, model parameters for the generator and for the discriminator;
- and
- after training the generator and discriminator, deploying, by the programmed computer system, the discriminator to determine whether a content sample presented to the discriminator is authentic.
7 more elements
A computer system for building a content authenticity validator, the computer system comprising:
- one or more processing units;
- and
- computer memory in communication with the one or more processing units, wherein the computer memory stores instructions that when executed by the one or more processing units cause the one or more processing units to:
- train, through machine learning, a generator and a discriminator together in a multi-player, multi-round simulated game, where in each round of the multi-player, multi-round simulated game, the discriminator is trained to determine whether a selected data item, presented to the discriminator, is from the generator or from a data source that is different from the generator, wherein the computer memory stores instructions that cause the one or more processing units to train the generator and discriminator together by, in part:
- training the discriminator to perform first and second tasks, wherein:
- the first task is whether the selected data item is from the generator or from the data source;
- and
- the second task is whether the selected data item is authentic;
- training the generator to generate data that the discriminator incorrectly determines are not from the generator;
- and
- updating, iteratively and non-simultaneously, model parameters for the generator and for the discriminator;
- and
- after training the generator and discriminator, deploy the discriminator to determine whether a content sample presented to the discriminator is authentic.
10 more elements
Dependencies are inferred from the claim text.