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AssigneeInsurance Services Office, Inc.
InventorsAurobrata Ghosh, Steve Cruz, Terrance E. Boult, Maneesh Kumar Singh, Venkata Subbarao Veeravarasapu, Zheng Zhong
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A system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.
A machine learning system for localizing image forgery comprising:
A method for localizing image forgery by a machine learning system, comprising the steps of:
A non-transitory computer readable medium having instructions stored thereon for localizing image forgery by a machine learning system which, when executed by a processor, causes the processor to carry out the steps of:
Dependencies are inferred from the claim text.