公开(公告)号：US20230040237A1

公开(公告)日：2023-02-09

申请号：US17876908

申请日：2022-07-29

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Hua-mei CHEN ,  Bora SUL ,  Genshe CHEN ,  Erik BLASCH ,  Khanh PHAM

IPC: G06T7/00 ,  G06V10/80 ,  G06V10/82

Abstract: A method for detecting fake images includes: obtaining an image for authentication, and hand-crafting a multi-attribute classifier to determine whether the image is authentic. Hand-crafting the multi-attribute classifier includes fusing at least an image classifier, an image spectrum classifier, a co-occurrence matrix classifier, and a one-dimensional (1D) power spectrum density (PSD) classifier. The multi-attribute classifier is trained by pre-processing training images to generate an attribute-specific training dataset to train each of the image classifier, the image spectrum classifier, the co-occurrence matrix classifier, and the 1D PSD classifier.

公开(公告)号：US20220092420A1

公开(公告)日：2022-03-24

申请号：US17480999

申请日：2021-09-21

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Jingyang LU ,  Erik BLASCH ,  Roman ILIN ,  Hua-mei CHEN ,  Dan SHEN ,  Nichole SULLIVAN ,  Genshe CHEN

IPC: G06N3/08 ,  G06K9/62 ,  G06K9/00

Abstract: Embodiments of the present disclosure provide a method, a device, and a storage medium for domain adaptation for efficient learning fusion (DAELF). The method includes acquiring data from a plurality of data sources of a plurality of sensors; for each of the plurality of sensors, training an auxiliary classifier generative adversarial network (AC-GAN) by a hardware processor with data from each data source of the plurality of data sources, thereby obtaining a trained feature extraction network and a trained label prediction network for each data source; forming a decision-level fusion network or a feature-level fusion network; and training the decision-level fusion network or the feature-level fusion network with a source-only mode or a generate to adapt (GTA) mode; and applying the trained decision-level fusion network or the trained feature-level fusion network to detect a target of interest.

公开(公告)号：US20240185555A1

公开(公告)日：2024-06-06

申请号：US17474516

申请日：2021-09-14

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Hua-mei CHEN ,  Ashley DIEHL ,  Erik BLASCH ,  Genshe CHEN

IPC: G06V10/44 ,  G06V10/764

CPC classification number: G06V10/44 ,  G06V10/764

Abstract: Embodiments of the present disclosure provide a method, a device, and a storage medium for targeted adversarial discriminative domain adaptation (T-ADDA). The method includes pre-training a source model including a source feature encoder and a source classifier, adapting a target feature encoder, and generating a target model by concatenating the adapted target feature encoder with the pre-trained source classifier. Adapting the target feature encoder includes configuring the pre-trained source feature encoder to be an initial target feature encoder for generating target feature vectors in each target class; adjusting a domain discriminator according to an adversarial domain discrimination loss; adjusting the initial target feature encoder according to a generative adversarial network (GAN) loss; and further adjusting the initial target feature encoder to generate the target feature encoder according to a feature class matching loss using labeled target feature vectors and corresponding source feature class centers.