Mayachitra, Inc.

Google Inc.

Michael Gene Goebel

Bangalore S Manjunath

Shivkumar Chandrasekaran

Tejaswi Nanjundaswamy

Lakshmanan Nataraj

Tajuddin Manhar Mohammed

Dake He

Feng Yang

Xiyang Luo

Ryan Matthew Haggarty

Elnaz Barshan Tashnizi

Chandrakanth Gudavalli

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder that generates a watermark and a decoder that decodes a data item encoded within the watermark. The training comprises obtaining a plurality of training images and data items. For each training image, a first watermark is generated using an encoder and a subsequent second watermark is generated by tiling two or more first watermarks. The training image is watermarked using the second watermark to generate a first error value and distortions are added to the watermarked image. A distortion detector predicts the distortions based on which the distorted image is modified. The modified image is decoded by the decoder to generate a predicted data item and a second error value. The training parameters of the encoder and decoder are adjusted based on the first and the second error value.

End-to-end watermarking system

Systems, devices, methods and instructions are described for detecting GAN generated images. On embodiment involves receiving an images, generating co-occurrence matrices on color channels of the image, generating analysis of the image by using a convolutional neural network trained to analyze image features of the images based on the generated co-occurrence matrices and determining whether the image is a GAN generated image based on the analysis.

Detecting digital human face image manipulations

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe a metadata verification system that is configured to access a digital satellite image, generate a first discrete Fourier transform (DFT) pattern based on an ortho-rectified digital satellite image, generate a second DFT pattern based on rational polynomial coefficient (RPC) data of the digital satellite image, compare the first DFT pattern to the second DFT pattern, generate a score based on the comparison, and generate a determination of whether the digital satellite image has been manipulated based on the score.

Rational polynomial coefficient based metadata verification

Detecting digital image manipulations

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Santa Barbara, CA, United States of America

Michael Gene Goebel