Computer Vision Systems and Methods for Blind Localization of Image Forgery
AssigneeInsurance Services Office, Inc.
InventorsAurobrata Ghosh, Zhong Zheng, Terrance E. Boult, Maneesh Kumar Singh
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Abstract
Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.
Claims19
A computer vision system for localizing image forgery comprising:
- a memory;
- and
- a processor in communication with the memory, the processor:
- generating a constrained convolution using a plurality of learned rich filters,
- training a neural network with the constrained convolution and a plurality of images of a dataset to learn a low-level representation indicative of a statistical signature of at least one source camera model for each image among the plurality of images, and
- localizing an attribute of an image of the dataset by the trained neural network.
A method for localizing image forgery by a computer vision system, comprising the steps of:
- generating a constrained convolution via a plurality of learned rich filters,
- training a neural network with the constrained convolution and a plurality of images of a dataset to learn a low-level representation indicative of a statistical signature of at least one source camera model for each image among the plurality of images, and
- localizing an attribute of an image of the dataset by the trained neural network.
A non-transitory computer readable medium having instructions stored thereon for localizing image forgery by a computer vision system which, when executed by a processor, causes the processor to carry out the steps of:
- generating a constrained convolution via a plurality of learned rich filters,
- training a neural network with the constrained convolution and a plurality of images of a dataset to learn a low-level representation indicative of a statistical signature of at least one source camera model for each image among the plurality of images, and
- localizing an attribute of an image of the dataset by the trained neural network.
Dependencies are inferred from the claim text.