Abstract: Systems and methods herein describe an image generation system that accesses a semantic map and satellite image, manipulates the semantic map image, trains a machine learning framework using a set of map and satellite image pairs, uses the trained machine learning framework to generate a manipulated satellite image based on the manipulated semantic map, generates a blended satellite image based on a combination of the manipulated satellite image data and the originally accessed satellite image data, and stores the blended satellite image.

Abstract: Methods, systems and computer programs are presented for classifying malware using audio and image signal processing. One method includes an operation for converting a software application to an audio signal and an image signal. Audio features are extracted from the audio signal and image features are extracted from the image signal and are used to classify the software application.

Abstract: Systems and methods herein describe a malware visualization system that is configured to access a computer file, generate a first image of the computer file, determine a frequency count of bi-grams in the computer file, compute a discrete cosine transform (DCT) of the frequency count of bi-grams, generate a second image of the computer file based on the DCT of the frequency count of bi-grams, analyze the first image and the second image using an image classification neural network and generate a classification of the computer file.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods, systems and computer programs are presented for classifying malware using audio signal processing. One method includes an operation for converting a non-audio data file to an audio signal. Audio features are extracted from the audio signal and are used to classify the non-audio data file.