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  • Open Access


    Generative Adversarial Networks for Secure Data Transmission in Wireless Network

    E. Jayabalan*, R. Pugazendi

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3757-3784, 2023, DOI:10.32604/iasc.2023.031200

    Abstract In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an… More >

  • Open Access


    Artificially Generated Facial Images for Gender Classification Using Deep Learning

    Valliappan Raman1, Khaled ELKarazle2,*, Patrick Then2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1341-1355, 2023, DOI:10.32604/csse.2023.026674

    Abstract Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common. A typical gender classifier requires many training samples to learn as many distinguishable features as possible. However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law. In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset of facial images is proposed.… More >

  • Open Access


    Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

    Anas Al-Ragehi1, Said Jadid Abdulkadir1,2,*, Amgad Muneer1,2, Safwan Sadeq3, Qasem Al-Tashi4,5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2169-2186, 2022, DOI:10.32604/cmc.2022.027885

    Abstract Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce satisfactory results. Therefore, this study… More >

  • Open Access


    Optimized Generative Adversarial Networks for Adversarial Sample Generation

    Daniyal M. Alghazzawi1, Syed Hamid Hasan1,*, Surbhi Bhatia2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3877-3897, 2022, DOI:10.32604/cmc.2022.024613

    Abstract Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP)… More >

  • Open Access


    Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

    Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143

    Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a technique used for increasing the… More >

  • Open Access


    GAN-GLS: Generative Lyric Steganography Based on Generative Adversarial Networks

    Cuilin Wang1, Yuling Liu1,*, Yongju Tong1, Jingwen Wang2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1375-1390, 2021, DOI:10.32604/cmc.2021.017950

    Abstract Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers. Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GAN-based steganography methods that are less dependent on text. In this paper, we propose a new method of generative lyrics steganography based on GANs, called GAN-GLS. The proposed method uses the GAN model and the large-scale lyrics corpus to construct and train a lyrics generator. In this method, the GAN uses a previously generated line of a lyric as the input sentence in order to generate the next line of the lyric.… More >

  • Open Access


    Speech Enhancement via Residual Dense Generative Adversarial Network

    Lin Zhou1,*, Qiuyue Zhong1, Tianyi Wang1, Siyuan Lu1, Hongmei Hu2

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 279-289, 2021, DOI:10.32604/csse.2021.016524

    Abstract Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features… More >

  • Open Access


    Image-to-Image Style Transfer Based on the Ghost Module

    Yan Jiang1, Xinrui Jia1, Liguo Zhang1,2,*, Ye Yuan1, Lei Chen3, Guisheng Yin1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4051-4067, 2021, DOI:10.32604/cmc.2021.016481

    Abstract The technology for image-to-image style transfer (a prevalent image processing task) has developed rapidly. The purpose of style transfer is to extract a texture from the source image domain and transfer it to the target image domain using a deep neural network. However, the existing methods typically have a large computational cost. To achieve efficient style transfer, we introduce a novel Ghost module into the GANILLA architecture to produce more feature maps from cheap operations. Then we utilize an attention mechanism to transform images with various styles. We optimize the original generative adversarial network (GAN) by using more efficient calculation… More >

  • Open Access


    A Generative Adversarial Networks for Log Anomaly Detection

    Xiaoyu Duan1, Shi Ying1,*, Wanli Yuan1, Hailong Cheng1, Xiang Yin2

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030

    Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The discriminator uses the Convolutional Neural… More >

  • Open Access


    Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

    Feng Zhang1, Huihuang Zhao1,2,*, Wang Ying1,2, Qingyun Liu1,2, Alex Noel Joseph Raj3, Bin Fu4

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1391-1401, 2020, DOI:10.32604/iasc.2020.011750

    Abstract Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on… More >

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