Abstract: A system and a method for detecting image forgery through a convolutional neural network are capable of detecting image manipulation of compressed and/or color images. The system comprises a manipulated feature pre-processing unit applying an input image to a high-pass filter to enhance features due to image forgery; a manipulated feature extraction unit extracting image manipulated feature information from the image with the enhanced features through a pre-trained convolutional neural network; a feature refining unit refining the extracted image manipulated feature information; and a manipulation classifying unit determining the image forgery based on the image manipulated feature information refined by the feature refining unit.

Abstract: A computer device for providing a virtual try-on image includes a camera interface connected to a camera, a display interface connected to a display device, and a processor configured to communicate with the camera through the camera interface and communicate with the display device through the display interface. The processor is configured to receive input images generated by the camera photographing a user through the camera interface, by processing a user object obtained from one of the input images, generate pose estimation data representing a pose of the user object, select an input image having the user object of which pose represented by the pose estimation data matches a reference pose, generate the virtual try-on image by synthesizing a clothes object with the user object included in the selected input image, and visualize the virtual try-on image by controlling the display device through the display interface.

Abstract: A method of a clothing virtual try-on service based on deep learning allowing a virtual try-on service application executed by at least one processor of a computing device to perform a process of the clothing virtual try-on service based on the deep-learning includes: determining a first clothes image including a first clothes object, a second clothes image including a second clothes object, and a model image including a model object; generating a first transformed clothes image by transforming a shape of the first clothes object to correspond to the model object; generating a second transformed clothes image by transforming a shape of the second clothes object to correspond to the model object; and generating and outputting a virtual fitting image obtained by synthesizing the first transformed clothes image and the second transformed clothes image to be virtually fitted on the model object.

Abstract: A deep learning-based virtual hair dyeing method is performed by an image editing server including a deep learning neural network and generative adversarial networks (GANs). The method comprises obtaining an original image and a desired hair color; generating a semantic map by providing the original image to the deep learning neural network; generating a trimap automatically with respect to the original image based on the semantic map; generating an alpha mask by performing alpha matting on the original image based on the trimap; obtaining a transformed image by applying the original image to the GANs; and obtaining a virtual dyeing image by synthesizing the original image and the obtained transformed image through the alpha mask.

Abstract: A deep learning-based virtual hair dyeing method is performed by an image editing server including a deep learning neural network and generative adversarial networks (GANs). The method comprises obtaining an original image and a desired hair color; generating a semantic map by providing the original image to the deep learning neural network; generating a trimap automatically with respect to the original image based on the semantic map; generating an alpha mask by performing alpha matting on the original image based on the trimap; obtaining a transformed image by applying the original image to the GANs; and obtaining a virtual dyeing image by synthesizing the original image and the obtained transformed image through the alpha mask.

Abstract: A system and a method for detecting image forgery through a convolutional neural network are capable of detecting image manipulation of compressed and/or color images. The system comprises a manipulated feature pre-processing unit applying an input image to a high-pass filter to enhance features due to image forgery; a manipulated feature extraction unit extracting image manipulated feature information from the image with the enhanced features through a pre-trained convolutional neural network; a feature refining unit refining the extracted image manipulated feature information; and a manipulation classifying unit determining the image forgery based on the image manipulated feature information refined by the feature refining unit.