Patents by Inventor Gaurav Goswami

Gaurav Goswami has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12093839
    Abstract: An entity learning recognition method, system, and computer program product include learning (i.e., in a training phase) from at least one entity to produce augments entities such that an augmented entity is still recognizable as the original entity but differs sufficiently to produce a different feature representation of the entity to create a database for use (i.e., in an implementation phase).
    Type: Grant
    Filed: April 29, 2021
    Date of Patent: September 17, 2024
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha
  • Patent number: 11941536
    Abstract: An entity learning recognition method and computer program product include learning training a model based on a combination of an original entity and an augmented entity in an augmented database, where the entity includes an image that is used for a training of the model and where the training is based on a visual element portion of the image with added noise.
    Type: Grant
    Filed: September 23, 2021
    Date of Patent: March 26, 2024
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha
  • Patent number: 11763159
    Abstract: A neural network is configured to suppress an output of a mitigation node in a mitigation layer of the neural network. The neural network is pre-configured to recognize objects from inputs when operating using a processor and a memory. An actual input is sent to the neural network for object recognition, the actual input is an altered input. By suppressing the output of the mitigation node, the neural network is caused to avoid falsely recognizing an object from the actual input, where the altered input is configured to cause the neural network to falsely recognize the object from the actual input.
    Type: Grant
    Filed: January 29, 2018
    Date of Patent: September 19, 2023
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gaurav Goswami, Sharathchandra Pankanti, Nalini K. Ratha, Richa Singh, Mayank Vatsa
  • Patent number: 11681918
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Grant
    Filed: April 21, 2021
    Date of Patent: June 20, 2023
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20220012543
    Abstract: An entity learning recognition method and computer program product include learning training a model based on a combination of an original entity and an augmented entity in an augmented database, where the entity includes an image that is used for a training of the model and where the training is based on a visual element portion of the image with added noise.
    Type: Application
    Filed: September 23, 2021
    Publication date: January 13, 2022
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha
  • Patent number: 11188798
    Abstract: Multiple trained AI models are tested using known genuine samples of respective multiple modalities of multimedia to generate versions of the multiple modalities of a given multimedia sample. Data for the multimedia and the multimedia sample are divided into the multiple modalities. Respective differences are computed between respective components of the multiple trained AI models to produce respective multiple difference vector, which are compared with corresponding baseline difference vectors determined in order to train the multiple trained AI models. The given multimedia sample is classified as genuine or altered using at least the comparison.
    Type: Grant
    Filed: May 21, 2020
    Date of Patent: November 30, 2021
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210365714
    Abstract: Multiple trained AI models are tested using known genuine samples of respective multiple modalities of multimedia to generate versions of the multiple modalities of a given multimedia sample. Data for the multimedia and the multimedia sample are divided into the multiple modalities. Respective differences are computed between respective components of the multiple trained AI models to produce respective multiple difference vector, which are compared with corresponding baseline difference vectors determined in order to train the multiple trained AI models. The given multimedia sample is classified as genuine or altered using at least the comparison.
    Type: Application
    Filed: May 21, 2020
    Publication date: November 25, 2021
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20210264268
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Application
    Filed: April 21, 2021
    Publication date: August 26, 2021
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Patent number: 11093796
    Abstract: An entity learning recognition method, system, and computer program product include learning (i.e., in a training phase) from at least one entity to produce augments entities such that an augmented entity is still recognizable as the original entity but differs sufficiently to produce a different feature representation of the entity to create a database for use (i.e., in an implementation phase).
    Type: Grant
    Filed: March 29, 2017
    Date of Patent: August 17, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha
  • Publication number: 20210248424
    Abstract: An entity learning recognition method, system, and computer program product include learning (i.e., in a training phase) from at least one entity to produce augments entities such that an augmented entity is still recognizable as the original entity but differs sufficiently to produce a different feature representation of the entity to create a database for use (i.e., in an implementation phase).
    Type: Application
    Filed: April 29, 2021
    Publication date: August 12, 2021
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha
  • Patent number: 11042799
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Grant
    Filed: August 20, 2019
    Date of Patent: June 22, 2021
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Patent number: 10956778
    Abstract: A system, method and program product for implementing image recognition. A system is disclosed that includes a training system for generating a multi-feature multi-matcher fusion (MMF) predictor for scoring pairs of images, the training system having: a neural network configurable to extract a set of feature spaces at different resolutions based on a training dataset; and an optimizer that processes the training dataset, extracted feature spaces and a set of matcher functions to generate the MMF predictor having a series of weighted feature/matcher components; and a prediction system that utilizes the MMF predictor to generate a prediction score indicative of a match for a pair of images.
    Type: Grant
    Filed: March 5, 2019
    Date of Patent: March 23, 2021
    Assignee: International Business Machines Corporation
    Inventors: Nalini K. Ratha, Gaurav Goswami, Sharathchandra U. Pankanti
  • Patent number: 10944767
    Abstract: Mechanisms are provided for training a classifier to identify adversarial input data. A neural network processes original input data representing a plurality of non-adversarial original input data and mean output learning logic determines a mean response for each intermediate layer of the neural network based on results of processing the original input data. The neural network processes adversarial input data and layer-wise comparison logic compares, for each intermediate layer of the neural network, a response generated by the intermediate layer based on processing the adversarial input data, to the mean response associated with the intermediate layer, to thereby generate a distance metric for the intermediate layer. The layer-wise comparison logic generates a vector output based on the distance metrics that is used to train a classifier to identify adversarial input data based on responses generated by intermediate layers of the neural network.
    Type: Grant
    Filed: February 1, 2018
    Date of Patent: March 9, 2021
    Assignee: International Business Machines Corporation
    Inventors: Gaurav Goswami, Sharathchandra Pankanti, Nalini K. Ratha, Richa Singh, Mayank Vatsa
  • Publication number: 20210056404
    Abstract: Mechanisms are provided to provide an improved computer tool for determining and mitigating the presence of adversarial inputs to an image classification computing model. A machine learning computer model processes input data representing a first image to generate a first classification output. A cohort of second image(s), that are visually similar to the first image, is generated based on a comparison of visual characteristics of the first image to visual characteristics of images in an image repository. A cohort-based machine learning computer model processes the cohort of second image(s) to generate a second classification output and the first classification output is compared to the second classification output to determine if the first image is an adversarial image. In response to the first image being determined to be an adversarial image, a mitigation operation by a mitigation system is initiated.
    Type: Application
    Filed: August 20, 2019
    Publication date: February 25, 2021
    Inventors: Gaurav Goswami, Nalini K. Ratha, Sharathchandra Pankanti
  • Publication number: 20200285914
    Abstract: A system, method and program product for implementing image recognition. A system is disclosed that includes a training system for generating a multi-feature multi-matcher fusion (MMF) predictor for scoring pairs of images, the training system having: a neural network configurable to extract a set of feature spaces at different resolutions based on a training dataset; and an optimizer that processes the training dataset, extracted feature spaces and a set of matcher functions to generate the MMF predictor having a series of weighted feature/matcher components; and a prediction system that utilizes the MMF predictor to generate a prediction score indicative of a match for a pair of images.
    Type: Application
    Filed: March 5, 2019
    Publication date: September 10, 2020
    Inventors: Nalini K. Ratha, Gaurav Goswami, Sharathchandra U. Pankanti
  • Publication number: 20190238568
    Abstract: Mechanisms are provided for training a classifier to identify adversarial input data. A neural network processes original input data representing a plurality of non-adversarial original input data and mean output learning logic determines a mean response for each intermediate layer of the neural network based on results of processing the original input data. The neural network processes adversarial input data and layer-wise comparison logic compares, for each intermediate layer of the neural network, a response generated by the intermediate layer based on processing the adversarial input data, to the mean response associated with the intermediate layer, to thereby generate a distance metric for the intermediate layer. The layer-wise comparison logic generates a vector output based on the distance metrics that is used to train a classifier to identify adversarial input data based on responses generated by intermediate layers of the neural network.
    Type: Application
    Filed: February 1, 2018
    Publication date: August 1, 2019
    Inventors: Gaurav Goswami, Sharathchandra Pankanti, Nalini K. Ratha, Richa Singh, Mayank Vatsa
  • Publication number: 20190236402
    Abstract: A neural network is configured to suppress an output of a mitigation node in a mitigation layer of the neural network. The neural network is pre-configured to recognize objects from inputs when operating using a processor and a memory. An actual input is sent to the neural network for object recognition, the actual input is an altered input. By suppressing the output of the mitigation node, the neural network is caused to avoid falsely recognizing an object from the actual input, where the altered input is configured to cause the neural network to falsely recognize the object from the actual input.
    Type: Application
    Filed: January 29, 2018
    Publication date: August 1, 2019
    Applicants: International Business Machines Corporation, Indraprastha Institute of Information Technology (IIIT), Delhi
    Inventors: Gaurav Goswami, Sharathchandra Pankanti, Nalini K. Ratha, Richa Singh, Mayank Vatsa
  • Publication number: 20180285690
    Abstract: An entity learning recognition method, system, and computer program product include learning (i.e., in a training phase) from at least one entity to produce augments entities such that an augmented entity is still recognizable as the original entity but differs sufficiently to produce a different feature representation of the entity to create a database for use (i.e., in an implementation phase).
    Type: Application
    Filed: March 29, 2017
    Publication date: October 4, 2018
    Inventors: Gaurav Goswami, Sharathchandra Umapathirao Pankanti, Nalini K. Ratha