Training a content authenticity validator as a variable resolution game

Patent metadata

Field	Value
Publication number	US-2026099720-A1
Application number	US-202418926575-A
Country	US
Kind code	A1
Filing date	Oct 25, 2024
Priority date	Mar 31, 2022
Publication date	Apr 9, 2026
Grant date	—

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.

First claim

What is claimed is:

1 . 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.

2 . The method of claim 1 , wherein:
the multi-player, multi-round simulated game comprises at least a round A and a round B, where round B is after round A; the discriminator employs a discriminator mixed strategy in the multi-player, multi-round simulated game; the generator employs a generator mixed strategy in the multi-player, multi-round simulated game; and the training comprises:
updating the discriminator mixed strategy in round A; and
updating the generator mixed strategy in round B.

3 . The method of claim 1 , wherein the content sample comprises a text sample.

4 . The method of claim 1 , wherein the content sample comprises an audio sample.

5 . The method of claim 1 , wherein the content sample comprises a video sample.

6 . The method of claim 1 , wherein the content sample comprises an image.

7 . The method of claim 1 , wherein the multi-player, multi-round, simulated game comprises a two-person, zero-sum game.

8 . The method of claim 7 , wherein the two-person, zero-sum game comprises a two-person, finite zero-sum game.

9 . The method of claim 2 , wherein:
updating the discriminator mixed strategy in round A comprises updating a current mixed strategy for the discriminator based on payoffs from rounds of the simulated game prior to round A; and updating the generator mixed strategy in round B comprises updating a current mixed strategy for the generator based on payoffs from rounds of the simulated game prior to round B.

10 . The method of claim 9 , wherein:
updating the discriminator mixed strategy in round A comprises finding a pure strategy for the discriminator that performs better against a then-current generator mixed strategy than does the current mixed strategy of the discriminator; and updating the generator mixed strategy in round B comprises finding a pure strategy for the generator that performs better against a then-current discriminator mixed strategy than does the current mixed strategy of the generator.

11 . The method of claim 2 , wherein:
the discriminator comprises a plurality of local region detectors; each of the plurality of local region detectors is trained, through machine learning, to discriminate whether a presented data item to the local region detector is accepted or rejected as being a member of a specified set associated with the local region detector; and updating the discriminator mixed strategy in round A comprises selecting a selected local region detector of the plurality of local region detectors, such that a first data item from the data source is based on the selected local region detector.

12 . The method of claim 11 , wherein:
the plurality of local region detectors comprises at least a first local region detector and a second local region detector; and a specified set for the first local region detector overlaps in part with a specified set for the second local region detector.

13 . The method of claim 12 , wherein:
the generator comprises a plurality of local generators; each of the plurality of local generators is trained, through machine learning, to generate data items that are in a local data region associated with the local generator; and updating the generator mixed strategy in round B comprises selecting a selected local generator of the plurality of local generators, such that data item generated by the generator in round B is generated by the selected local generator.

14 . The method of claim 13 , wherein:
the discriminator comprises a plurality of local discriminators; and each of the plurality of local discriminators is trained to determine whether a data item presented to the local discriminator is from the generator.

15 . The method of claim 14 , wherein:
each of the plurality of local generators comprises a neural network; each of the plurality of local region detectors comprises a neural network; and each of the plurality of local discriminators comprises a neural network.

16 . The method of claim 15 , further comprising, prior round A:
training, with the programmed computer system, through machine learning, the plurality of local generators; training, with the programmed computer system, through machine learning, the plurality of local region detectors; and training, with the programmed computer system, through machine learning, the plurality of local discriminators.

17 . The method of claim 1 , wherein the data source comprises a cooperative generator that is trained to be cooperative with the discriminator.

18 . The method of claim 1 , wherein:
the programmed computer system comprises a plurality of graphical processing units (GPUs); and training the generator and discriminator together comprises processing training data in parallel with the plurality of GPUs.

19 . 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

Assignees

• D5Ai Llc

Inventors

• Baker James K

Classifications

• G06N3/045

  Combinations of networks · CPC title

• A63F13/67

  adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title