Abstract: A graft copolymer, and a graft copolymer composition having excellent particulate dispersibility in a curable resin such as an epoxy resin and is applicable as a particulate impact reinforcing agent, a curable resin composition including same, and methods of preparing them.

Abstract: A method and apparatus for training a fake image discriminative model according to an embodiment of the present disclosure includes generating one or more fake images for a real image by selecting one or more encoding layers and one or more decoding layers from a generator network of an autoencoder structure, generating a training image set based on the one or more fake images, and training a classifier for discriminating a fake image by using the training image set.

Abstract: An apparatus for reading an image executes operations including receiving a plurality of images for a same subject from a photographing device, obtaining a screen division value related to the plurality of images, checking focused sections of the plurality of images based on the obtained screen division value, and determining type of the plurality of images based on a result of checking the focused sections. According to the apparatus, it can be determined whether an image is actually photographed without a separate ToF module.

Abstract: A synthetic data generation apparatus according to an embodiment includes a generator for generating synthetic data from an input value, a first discriminator learned to distinguish between actual data and the synthetic data, a second discriminator learned to distinguish between the actual data and the synthetic data while satisfying differential privacy, and a third discriminator learned to distinguish between first synthetic data which is output from the generator learned by the first discriminator and second synthetic data which is output from the generator learned by the second discriminator.

Abstract: An apparatus for reading an image executes operations including receiving a plurality of images for a same subject from a photographing device, obtaining a screen division value related to the plurality of images, checking focused sections of the plurality of images based on the obtained screen division value, and determining type of the plurality of images based on a result of checking the focused sections. According to the apparatus, it can be determined whether an image is actually photographed without a separate ToF module.

Abstract: A method for photographing an image performed by a computer device according to an embodiment of the present application includes obtaining a screen division value for photographing an image, classifying a photographing screen into a plurality of sections based on the screen division value, photographing a first image by focusing a first section among the plurality of sections, photographing a second image by focusing a second section among the plurality of sections and transmitting the first image and the second image to a server. According to the method, it is capable of reading whether an image is actually photographed without having a separate ToF module, and applicable to an outdoor environment on which interference from other light rays is strong.

Abstract: An apparatus for deep fake image discrimination according to an embodiment includes an interface unit configured to receive image data and a classifier configured to determine whether the image data input through the interface unit is a deep fake image, and the classifier is trained to determine a deep fake image based on a synthetic image generated by swapping a portion of a real image with a fake image generated by self-replicating the real image.

Abstract: A method for preventing forgery of data according to an embodiment includes determining a noise level based on metadata of original data, generating noise by applying the determined noise level to a preset noise pattern, generating transformed data of the original data by adding the generated noise to the original data, and transmitting the transformed data and the metadata to a server.

Abstract: An apparatus for classifying fake images according to an embodiment of the present disclosure includes an artifact remover configured to receive an input image to generate an artifact-removed image from which artifacts are removed, an artifact image generator configured to generate an artifact image by using a difference between the input image and the artifact removal image, and a determiner configured to determine whether the artifact image is a real image or a fake image by receiving the artifact image.

Abstract: An apparatus for anonymizing personal information according to an embodiment may include an encoder configured to generate an input latent vector by extracting a feature of input data, a generator configured to generate reconstructed data by demodulating a predetermined content vector based on a style vector generated based on the input latent vector, and a discriminator configured to discriminate genuine data and fake data by receiving the reconstructed data and real data.

Abstract: An apparatus for learning of generative model according to an embodiment includes an encoder configured to extract a feature from input data and output a feature vector, a decoder configured to restore the input data on the basis of the feature vector, and a domain module configured to generate domain information to be learned through a generative model into domain vector blocks, each with a size corresponding to the feature vector, concatenate the feature vector and the domain vector block, and input a concatenated vector to the decoder.

Abstract: A method for preventing forgery of data according to an embodiment includes determining a noise level based on metadata of original data, generating noise by applying the determined noise level to a preset noise pattern, generating transformed data of the original data by adding the generated noise to the original data, and transmitting the transformed data and the metadata to a server.

Abstract: A method for photographing an image performed by a computer device according to an embodiment of the present application includes obtaining a screen division value for photographing an image, classifying a photographing screen into a plurality of sections based on the screen division value, photographing a first image by focusing a first section among the plurality of sections, photographing a second image by focusing a second section among the plurality of sections and transmitting the first image and the second image to a server. According to the method, it is capable of reading whether an image is actually photographed without having a separate ToF module, and applicable to an outdoor environment on which interference from other light rays is strong.

Abstract: A synthetic data generation apparatus according to an embodiment includes a generator for generating synthetic data from an input value, a first discriminator learned to distinguish between actual data and the synthetic data, a second discriminator learned to distinguish between the actual data and the synthetic data while satisfying differential privacy, and a third discriminator learned to distinguish between first synthetic data which is output from the generator learned by the first discriminator and second synthetic data which is output from the generator learned by the second discriminator.

Abstract: An apparatus for learning of generative model according to an embodiment includes an encoder configured to extract a feature from input data and output a feature vector, a decoder configured to restore the input data on the basis of the feature vector, and a domain module configured to generate domain information to be learned through a generative model into domain vector blocks, each with a size corresponding to the feature vector, concatenate the feature vector and the domain vector block, and input a concatenated vector to the decoder.

Abstract: The cutting tool of the present invention has a base material and a multi-layer coating formed thereon. The multi-layer coating includes an A-layer, a B-layer and a C-layer repeatedly deposited in the order of A-layer, C-layer and B-layer from the base material toward an outer surface of the multi-layer coating. The A-layer has a1 layers and a2 layers wherein 8-20 layers of a1 layers and a2 layers are non-periodically deposited per 100 nm. Each unit layer of the A-layer, B-layer, and C-layer has a thickness of 0.5-2.0 ?m, 0.1 ?m-0.5 ?m and 55-95 nm respectively.

Abstract: The cutting tool of the present invention has a base material and a multi-layer coating formed thereon. The multi-layer coating includes an A-layer, a B-layer and a C-layer repeatedly deposited in the order of A-layer, C-layer and B-layer from the base material toward an outer surface of the multi-layer coating. The A-layer has a1 layers and a2 layers wherein 8-20 layers of a1 layers and a2 layers are non-periodically deposited per 100 nm. Each unit layer of the A-layer, B-layer, and C-layer has a thickness of 0.5-2.0 ?m, 0.1 ?m-0.5 ?m and 55-95 nm respectively.