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  • Open Access


    A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

    Xin Liu*, Xiao Chen

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839

    Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On… More >

  • Open Access


    A 360-Degree Panoramic Image Inpainting Network Using a Cube Map

    Seo Woo Han, Doug Young Suh*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 213-228, 2021, DOI:10.32604/cmc.2020.012223

    Abstract Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it… More >

  • Open Access


    Edge Detection Based on Generative Adversarial Networks

    Xiaoyan Chen, Jiahuan Chen*, Zhongcheng Sha

    Journal of New Media, Vol.2, No.2, pp. 61-77, 2020, DOI:10.32604/jnm.2020.010062

    Abstract Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500… More >

  • Open Access


    Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

    Kui Fu1, Jiansheng Peng1, 2, *, Hanxiao Zhang2, Xiaoliang Wang3, Frank Jiang4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882

    Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

  • Open Access


    Better Visual Image Super-Resolution with Laplacian Pyramid of Generative Adversarial Networks

    Ming Zhao1, Xinhong Liu1, Xin Yao1, *, Kun He2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1601-1614, 2020, DOI:10.32604/cmc.2020.09754

    Abstract Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to restore finer texture details during image super-resolution reconstruction? This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the Laplacian pyramid to capture the high-frequency details of the image. By combining Laplacian pyramids and generative adversarial networks, progressive reconstruction of super-resolution images can be made, making model applications more flexible. In order to solve the problem of gradient disappearance, we introduce the Residual-in-Residual Dense… More >

  • Open Access


    Quantum Generative Adversarial Network: A Survey

    Tong Li1, Shibin Zhang1, *, Jinyue Xia2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

    Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More >

  • Open Access


    Few-Shot Learning with Generative Adversarial Networks Based on WOA13 Data

    Xin Li1,2, Yanchun Liang1,2, Minghao Zhao1,2, Chong Wang1,2,3, Yu Jiang1,2,*

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1073-1085, 2019, DOI:10.32604/cmc.2019.05929

    Abstract In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN… More >

  • Open Access


    Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network

    Chuanlong Li1,2,*, Yumeng Jiang3, Marta Cheslyar1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 313-324, 2018, DOI: 10.3970/cmc.2018.03950

    Abstract Deep neural network has proven to be very effective in computer vision fields. Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods. Generative adversarial network (GAN) is becoming one of the highlights among these deep neural networks. GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks. One promising application of using GAN generated images would be image concealing which requires the embedded image looks… More >

  • Open Access


    Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

    Ya Tu1, Yun Lin1, Jin Wang2,3,*, Jeong-Uk Kim4

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

    Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter… More >

  • Open Access


    Adversarial Learning for Distant Supervised Relation Extraction

    Daojian Zeng1,3, Yuan Dai1,3, Feng Li1,3, R. Simon Sherratt2, Jin Wang3,*

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 121-136, 2018, DOI:10.3970/cmc.2018.055.121

    Abstract Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute… More >

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