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DEEP NEURAL NETWORK BASED IDENTIFICATION OF REALISTIC SYNTHETIC IMAGES GENERATED USING A GENERATIVE ADVERSARIAL NETWORK
DEEP NEURAL NETWORK BASED IDENTIFICATION OF REALISTIC SYNTHETIC IMAGES GENERATED USING A GENERATIVE ADVERSARIAL NETWORK
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机译:基于深神经网络的基于神经网络使用生成对抗网络产生的现实合成图像的识别
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摘要
Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like
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