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1
Thakur, Amey. "Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2307–25. http://dx.doi.org/10.22214/ijraset.2021.37723.
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Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.
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Iranmanesh, Seyed Mehdi, and Nasser M. Nasrabadi. "HGAN: Hybrid generative adversarial network." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8927–38. http://dx.doi.org/10.3233/jifs-201202.
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In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.
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Bhandari, Basant Babu, Aakash Raj Dhakal, Laxman Maharjan, and Asmin Karki. "Nepali Handwritten Letter Generation using GAN." Journal of Science and Engineering 9 (December 31, 2021): 49–55. http://dx.doi.org/10.3126/jsce.v9i9.46308.
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The generative adversarial networks seem to work very effectively for training generative deep neural networks. The aim is to generate Nepali Handwritten letters using adversarial training in raster image format. Deep Convolutional generative network is used to generate Nepali handwritten letters. Proposed generative adversarial model that works on Devanagari 36 classes, each having 10,000 images, generates the Nepali Handwritten Letters that are similar to the real-life data-set of total size 360,000 images. The generated letters are obtained by simultaneously training the generator and discriminator of the network. Constructed discriminator networks and generator networks both have five convolution layers and the activation function is chosen such that generator networks generate the image and discriminator networks check if the generated image is similar to a real-life image dataset. To measure the quantitative performance, Frechet Inception Distance (FID) methodology is used. The FID value of 18 random samples, generated by networks constructed, is 38413677.145. For a qualitative measure of the model let the reader judge the quality of the image generated by the generator trained model. The Nepali letters were generated by the adversarial network as required. The evaluation helps the generative model to be better and further enables a better generation that humans have not thought of.
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Rajbeer, Kaur, Kaur Amanpreet, and Kaur Kirandeep. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. https://doi.org/10.5281/zenodo.13690760.
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The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.
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Huo, Lin, Huanchao Qi, Simiao Fei, Cong Guan, and Ji Li. "A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis." Computational Intelligence and Neuroscience 2022 (July 13, 2022): 1–21. http://dx.doi.org/10.1155/2022/7592258.
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As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedding, and applied to solve fault diagnosis problems with insufficient data. By analyzing the time-domain characteristics of rolling bearing life cycle monitoring data in actual working conditions, the operation data are divided into three periods, and the construction and training of the generative adversarial network model are carried out. Data generated by adversarial are compared with the real data in the time domain and frequency domain, respectively, and the similarity between the generated data and the real data is verified.
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Rajbeer Kaur, Amanpreet Kaur, and Kirandeep Kaur. "Enhancing generative adversarial network." Global Journal of Engineering and Technology Advances 19, no. 1 (2024): 068–73. http://dx.doi.org/10.30574/gjeta.2024.19.1.0057.
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The paper provides a comprehensive review of various GAN methods from the perspectives of theory, and applications. GAN algorithms' mathematical representations, and structures are detailed. The commonalities and differences among these GANs methods are compared. Theoretical issues related to GANs are explored, and typical applications in various fields are showcased. Future scope of research problems for GANs are also discussed in the paper.
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Huang, Wenqi, Lingyu Liang, Zhen Dai, et al. "Scenario Reduction of Power Systems with Renewable Generations Using Improved Time-GAN." Journal of Physics: Conference Series 2662, no. 1 (2023): 012009. http://dx.doi.org/10.1088/1742-6596/2662/1/012009.
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Abstract To investigate the uncertainties and spatiotemporal complexities of renewable energy represented by wind and photovoltaic, a scenario reduction of power systems with renewable generations uses improved time series generative adversarial networks (Time GAN). The long short-term memory neural network is used to construct the generative adversarial networks, and the time-series supervision loss function and generative adversarial loss function are introduced to jointly optimize the generator network for better generating the daily renewable energy power scenarios. Based on the results of scenario generation, the silhouette coefficient method is used to improve K-means for constructing a scenario reduction model. Finally, the case analysis shows that the proposed method can obtain typical renewable energy power scenarios with spatiotemporal correlation and provide a reference for the analysis of power system operation scenarios.
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Amir, Iqbal, Hamizan Suhaimi, Roslina Mohamad, Ezmin Abdullah, and Chuan-Hsian Pu. "Hybrid encryption based on a generative adversarial network." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 971. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp971-978.
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In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.
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Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.
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Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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Iqbal, Amir Hamizan Suhaimi Roslina Mohamad Ezmin Abdullah Chuan-Hsian Pu. "Hybrid encryption based on a generative adversarial network." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 971–78. https://doi.org/10.11591/ijeecs.v35.i2.pp971-978.
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In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.
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Manimegala M, Gokulraj V, Karisni K, and Manisha S. "Generating Human Face with Dcgan and Gan." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1348–54. http://dx.doi.org/10.47392/irjaeh.2024.0186.
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Generative Adversarial Networks (GANs) are prominent in unsupervised learning for their exceptional data-generation capabilities. GANs utilize backpropagation and a competitive process between a Generative Network (G) and a Discriminative Network (D). In this setup, G generates artificial images while D distinguishes real from artificial ones, enhancing G's ability to create realistic images. Deep Convolutional Generative Adversarial Networks (DCGAN) are particularly notable, using a convolutional architecture to produce high-quality human face images. This study trains DCGAN on the CelebFaces Attributes Dataset (CelebA), demonstrating its ability to generate human faces from unlabeled data and random noise. Evaluation is done quantitatively using the Structural Similarities Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) to assess image quality. Additionally, this abstract will compare the effectiveness of GANs and DCGANs in human face generation.
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Agarwal, Arun, Swatishee Chhotaray, Niraj Kumar Roul, and Saurabh N. Mehta. "A Review Super Resolution Using Generative Adversarial Network-Applications and Challenges." Middle East Research Journal of Engineering and Technology 3, no. 1 (2023): 1–6. http://dx.doi.org/10.36348/merjet.2023.v03i01.001.
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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.
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Priyanka, Kumari, Sneha Shrishti, and Siddhi Jain. "4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review." Middle East Research Journal of Engineering and Technology 3, no. 1 (2023): 7–11. http://dx.doi.org/10.36348/merjet.2023.v03i01.002.
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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.
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Mahima, Pandya, and Sonal Rami Prof. "IMAGE GENERATION FROM CAPTION." International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 7, no. 1/2 (2018): 01–10. https://doi.org/10.5281/zenodo.6631185.
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Generating images from a text description is as challenging as it is interesting. The Adversarial network performs in a competitive fashion where the networks are the rivalry of each other. With the introduction of Generative Adversarial Network, lots of development is happening in the field of Computer Vision. With generative adversarial networks as the baseline model, studied Stack GAN consisting of two-stage GANS step-by-step in this paper that could be easily understood. This paper presents visual comparative study of other models attempting to generate image conditioned on the text description. One sentence can be related to many images. And to achieve this multi-modal characteristic, conditioning augmentation is also performed. The performance of Stack-GAN is better in generating images from captions due to its unique architecture. As it consists of two GANS instead of one, it first draws a rough sketch and then corrects the defects yielding a high-resolution image.
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Deupa, Abhishek, Garima Saha, Nikhil S. Sharma, and Virendra Pal Singh. "Generative Adversarial Network Based Music Generation." Far Western Review 2, no. 1 (2024): 1–25. http://dx.doi.org/10.3126/fwr.v2i1.70491.
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Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.
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Lamba, Sahil, Himanshu ., Rohit Kumar Singh, and Er Kamal Soni. "Generative Adversarial Network (GAN) to Generate Realistic Images." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2190–96. http://dx.doi.org/10.22214/ijraset.2023.50306.
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Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results. Image synthesis is among the most thoroughly researched applications of GANs, and the results thus far have demonstrated the potential of GANs in image synthesis. This paper provides a taxonomy of the methods used in image synthesis, reviews various models for text-to-image synthesis and image-to-image translation, discusses evaluation metrics, and highlights future research directions for image synthesis using GANs..
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Lee, Minhyeok, and Junhee Seok. "Score-Guided Generative Adversarial Networks." Axioms 11, no. 12 (2022): 701. http://dx.doi.org/10.3390/axioms11120701.
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We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a rough guide for the training of the generator. Using another pretrained network instead of the Inception network, ScoreGAN circumvents overfitting of the Inception network such that the generated samples do not correspond to adversarial examples of the Inception network. In addition, evaluation metrics are employed only in an auxiliary role to prevent overfitting. When evaluated using the CIFAR-10 dataset, ScoreGAN achieved an Inception score of 10.36 ± 0.15, which corresponds to state-of-the-art performance. To generalize the effectiveness of ScoreGAN, the model was evaluated further using another dataset, CIFAR-100. ScoreGAN outperformed other existing methods, achieving a Fréchet Inception distance (FID) of 13.98.
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Yang, Ximing, Yuan Wu, Kaiyi Zhang, and Cheng Jin. "CPCGAN: A Controllable 3D Point Cloud Generative Adversarial Network with Semantic Label Generating." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3154–62. http://dx.doi.org/10.1609/aaai.v35i4.16425.
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Generative Adversarial Networks (GAN) are good at generating variant samples of complex data distributions. Generating a sample with certain properties is one of the major tasks in the real-world application of GANs. In this paper, we propose a novel generative adversarial network to generate 3D point clouds from random latent codes, named Controllable Point Cloud Generative Adversarial Network(CPCGAN). A two-stage GAN framework is utilized in CPCGAN and a sparse point cloud containing major structural information is extracted as the middle-level information between the two stages. With their help, CPCGAN has the ability to control the generated structure and generate 3D point clouds with semantic labels for points. Experimental results demonstrate that the proposed CPCGAN outperforms state-of-the-art point cloud GANs.
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Han, Ligong, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, and Dimitris Metaxas. "Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10909–16. http://dx.doi.org/10.1609/aaai.v34i07.6723.
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Conditional generative adversarial networks have shown exceptional generation performance over the past few years. However, they require large numbers of annotations. To address this problem, we propose a novel generative adversarial network utilizing weak supervision in the form of pairwise comparisons (PC-GAN) for image attribute editing. In the light of Bayesian uncertainty estimation and noise-tolerant adversarial training, PC-GAN can estimate attribute rating efficiently and demonstrate robust performance in noise resistance. Through extensive experiments, we show both qualitatively and quantitatively that PC-GAN performs comparably with fully-supervised methods and outperforms unsupervised baselines. Code and Supplementary can be found on the project website*.
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Chi, Wanle, Yun Huoy Choo, and Ong Sing Goh. "Review of Generative Adversarial Networks in Image Generation." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 1 (2022): 3–7. http://dx.doi.org/10.20965/jaciii.2022.p0003.
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Generative adversarial network (GAN) model generates and discriminates images using an adversarial competitive strategy to generate high-quality images. The implementation of GAN in different fields is helpful for generating samples that are not easy to obtain. Image generation can help machine learning to balance data and improve the accuracy of the classifier. This paper introduces the principles of the GAN model and analyzes the advantages and disadvantages of improving GANs. The applications of GANs in image generation are analyzed. Finally, the problems of GANs in image generation are summarized.
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Yang, LianShuQing, and Jiahui Li. "Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/8848307.
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Cultural symbol generation has always been a challenging task to achieve symbols that can represent national culture and promote people’s identification with Chinese culture. In this paper, we combine generative adversarial network (GAN) to propose a symbolic generation model of Chinese national cultural identity based on visual images. First, combining pattern search regular terms with generator cross-loss functions based on GAN generative adversarial networks to improve the pattern collapse phenomenon of generative adversarial networks. Second, the normal convolutional layer of the generator in the network structure is replaced with a deep-space separable convolution to improve the real-time performance of the model by reducing the model parameters. Through extensive testing on real datasets, the results show that the model in this paper can generate higher performance ethnic culture symbols while maintaining better temporal performance, which has some practical application value.
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Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.
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<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provide large coverage to achieve load balancing. By combing Generative Adversarial Network (GAN) with the ns-3 network simulator, this paper uses neural networks in TensorFlow to optimize load balancing of mobile networks, increase the data throughput and reduce the packet loss rate. In addition, to discuss the load balancing problem, we take the data produced by the ns-3 network simulator as the real data for GAN.</p> <p>&nbsp;</p>
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Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.
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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.
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Liu, Aishan, Xianglong Liu, Jiaxin Fan, et al. "Perceptual-Sensitive GAN for Generating Adversarial Patches." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1028–35. http://dx.doi.org/10.1609/aaai.v33i01.33011028.
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Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are still far from generating visually natural patches with strong attacking ability, since they often ignore the perceptual sensitivity of the attacked network to the adversarial patch, including both the correlations with the image context and the visual attention. To address this problem, this paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch. To improve the visual fidelity, we treat the patch generation as a patch-to-patch translation via an adversarial process, feeding any types of seed patch and outputting the similar adversarial patch with high perceptual correlation with the attacked image. To further enhance the attacking ability, an attention mechanism coupled with adversarial generation is introduced to predict the critical attacking areas for placing the patches, which can help producing more realistic and aggressive patches. Extensive experiments under semi-whitebox and black-box settings on two large-scale datasets GTSRB and ImageNet demonstrate that the proposed PS-GAN outperforms state-of-the-art adversarial patch attack methods.
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Long, Xinyue, and Mingchuan Zhang. "An Overview of Generative Adversarial Networks." Journal of Computing and Electronic Information Management 10, no. 3 (2023): 31–36. http://dx.doi.org/10.54097/jceim.v10i3.8677.
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Generative adversarial network (GAN), put forward by two-person zero-sum game theory, is one of the most important research hotspots in the field of artificial intelligence. With a generator network and a discriminator network, GAN is trained by adversarial learning. In this paper, we aim to discusses the development status of GAN. We first introduce the basic idea and training process of GAN in detail, and summarize the structure and structure of GAN derivative models, including conditional GAN, deep convolution DCGAN, WGAN based on Wasserstein distance and WGAN-GP based on gradient strategy. We also introduce the specific applications of GAN in the fields of information security, face recognition, 3D and video technology, and summarize the shortcomings of GAN. Finally, we look forward to the development trend of GAN.
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Tu, Jun, Willies Ogola, Dehong Xu, and Wei Xie. "Intrusion Detection Based on Generative Adversarial Network of Reinforcement Learning Strategy for Wireless Sensor Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 13, 2022): 478–82. http://dx.doi.org/10.46300/9106.2022.16.58.
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Due to the wireless nature of wireless sensor networks (WSN), the network can be deployed in most of the unattended environment, which makes the networks more vulnerable for attackers who may listen to the traffic and inject their own nodes in the sensor network. In our work, we research on a novel machine learning algorithm on intrusion detection based on reinforcement learning (RL) strategy using generative adversarial network (GAN) for WSN which can automatically detect intrusion or malicious attacks into the network. We combine Actor-Critic Algorithm in RL with GAN in a simulated WSN. The GAN is employed as part of RL environment to generate fake data with possible attacks, which is similar to the real data generated by the sensor networks. Its main aim is to confuse the adversarial network into differentiating between the real and fake data with possible attacks. The results that is from the experiments are based on environment of GAN and Network Simulator 3 (NS3) illustrate that Actor-Critic&GAN algorithm enhances security of the simulated WSN by protecting the networks data against adversaries and improves on the accuracy of the detection.
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Naman and Sudha Narang, Chaudhary Sarimurrab, Ankita Kesari. "Human Face Generation using Deep Convolution Generative Adversarial Network." January 2021 7, no. 01 (2021): 114–20. http://dx.doi.org/10.46501/ijmtst070127.
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The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.
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Chang, Yeong-Hwa, Pei-Hua Chung, Yu-Hsiang Chai, and Hung-Wei Lin. "Color Face Image Generation with Improved Generative Adversarial Networks." Electronics 13, no. 7 (2024): 1205. http://dx.doi.org/10.3390/electronics13071205.
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This paper focuses on the development of an improved Generative Adversarial Network (GAN) specifically designed for generating color portraits from sketches. The construction of the system involves using a GPU (Graphics Processing Unit) computing host as the primary unit for model training. The tasks that require high-performance calculations are handed over to the GPU host, while the user host only needs to perform simple image processing and use the model trained by the GPU host to generate images. This arrangement reduces the computer specification requirements for the user. This paper will conduct a comparative analysis of various types of generative networks which will serve as a reference point for the development of the proposed Generative Adversarial Network. The application part of the paper focuses on the practical implementation and utilization of the developed Generative Adversarial Network for the generation of multi-skin tone portraits. By constructing a face dataset specifically designed to incorporate information about ethnicity and skin color, this approach can overcome a limitation associated with traditional generation networks, which typically generate only a single skin color.
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Shi, Caijuan, Dongjing Tu, and Jingyi Liu. "Re-GAN: residual generative adversarial network algorithm." Journal of Image and Graphics 26, no. 3 (2021): 594–604. http://dx.doi.org/10.11834/jig.200069.
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Cai, Zhipeng, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, and Yi Pan. "Generative Adversarial Networks." ACM Computing Surveys 54, no. 6 (2021): 1–38. http://dx.doi.org/10.1145/3459992.
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Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.
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Rosline, Gnanam Jeba, and Pushpa Rani. "Intrusion detection based on generative adversarial network with random forest for cloud networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2491–98. https://doi.org/10.11591/ijece.v15i2.pp2491-2498.
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The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (GANs) based network abnormality recognition system is evaluated. It uses the CICIDS2018 dataset to detect intrusion. GAN is utilized to improve network anomaly detection in conjunction with an ensemble random forest (RF) classifier. The GAN-RF model achieved 95.01% of accuracy for intrusion detection and obtain better recall and F1-score. Extensive assessments and valuations illustrate the efficiency of the GAN-RF approach in accurately identifying network issues.
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Vibhute, Anup, Pranita Bhosale, Nikita Maralbhavi, Shailesh Galande, and Mokshada S. Bhandare. "Face Sketch to Image Generation using Generative Adversarial Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 669–75. http://dx.doi.org/10.17762/ijritcc.v11i10s.7706.
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Numerous studies have been conducted in the area of sketch to picture conversion and they got the good outcomes, but sometimes it is not accurate that they observed the blurry boundaries, the mixing of two colors that is the color of hair and face or mixing of both. These results are of the convolution neural networks that are basic of GAN. So to overcome their drawbacks we proposed a novel generative adversarial network using conditional GAN. For that we converted the original image in sketch and both the sketch and original image as reference is applied as input. We got more realistic and sharp colored images as compared to other. We focused on the feature detection, and the results are good. For the experimentation we used the STL-10 dataset. We overcome the problem of mixing of colors and got the different colors for hair, lips, and skin using conditional GAN as compared to CNN modern with increased performance and precision.
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Wu, Han. "Face image generation and feature visualization using deep convolutional generative adversarial networks." Journal of Physics: Conference Series 2634, no. 1 (2023): 012041. http://dx.doi.org/10.1088/1742-6596/2634/1/012041.
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Abstract Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential candidates while the GAN generates candidates. As a result, the discriminative network distinguishes created and real candidates, while the generative network learns to map from a latent space to an interest data distribution. In this article, the GAN model and some of its extensions will be thoroughly applied and implemented based on the dataset of CelebA, and details will be discussed through the images and graphs generated by the model. Specific training methods for various models and optimization algorithms can be produced by the GAN framework. The experiment’s findings in this article will show how the framework’s potential may be quantified and qualitatively assessed using the samples that were produced.
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Dharejo, Fayaz Ali, Farah Deeba, Yuanchun Zhou, et al. "TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (2021): 1–20. http://dx.doi.org/10.1145/3456726.
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Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.
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Wang, Xuan, Lijun Sun, Abdellah Chehri, and Yongchao Song. "A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images." Remote Sensing 15, no. 20 (2023): 5062. http://dx.doi.org/10.3390/rs15205062.
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High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.
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Rosline, Gnanam Jeba, and Pushpa Rani. "Intrusion detection based on generative adversarial network with random forest for cloud networks." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2491. https://doi.org/10.11591/ijece.v15i2.pp2491-2498.
Full textAbstract:
The development of cloud computing enables individuals and organizations to access a wide range of online programs and services. Because of its nature, numerous users can access and distribute cloud infrastructure. In cloud computing several security threats change the data and operations. A network's ability to detect malicious activity and possible threats is greatly aided by intrusion detection. To solve these issues, intrusion detection based on generative adversarial network with random forest (GAN-RF) for cloud networks is introduced. The function of the generative adversarial networks (GANs) based network abnormality recognition system is evaluated. It uses the CICIDS2018 dataset to detect intrusion. GAN is utilized to improve network anomaly detection in conjunction with an ensemble random forest (RF) classifier. The GAN-RF model achieved 95.01% of accuracy for intrusion detection and obtain better recall and F1-score. Extensive assessments and valuations illustrate the efficiency of the GAN-RF approach in accurately identifying network issues.
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Balint, Adam, and Graham Taylor. "Pal-GAN: Palette-conditioned Generative Adversarial Networks." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3536.
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Recent advances in Generative Adversarial Networks (GANs) have shown great progress on a large variety of tasks. A common technique used to yield greater diversity of samples is conditioning on class labels. Conditioning on high-dimensional structured or unstructured information has also been shown to improve generation results, e.g. Image-to-Image translation. The conditioning information is provided in the form of human annotations, which can be expensive and difficult to obtain in cases where domain knowledge experts are needed. In this paper, we present an alternative: conditioning on low-dimensional structured information that can be automatically extracted from the input without the need for human annotators. Specifically, we propose a Palette-conditioned Generative Adversarial Network (Pal-GAN), an architecture-agnostic model that conditions on both a colour palette and a segmentation mask for high quality image synthesis. We show improvements on conditional consistency, intersection-over-union, and Fréchet inception distance scores. Additionally, we show that sampling colour palettes significantly changes the style of the generated images.
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Zhang, Senwei. "Research Progress of Image Generation Methods Based on Deep Learning." Highlights in Science, Engineering and Technology 103 (June 26, 2024): 28–35. http://dx.doi.org/10.54097/0999vz62.
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This paper provides an overview of deep learning-based image generation methods and image style migration techniques. The focus is on text-to-image generation, unconditional and conditional image generation methods, and various approaches for image style migration. The review covers the underlying models, including Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion models, while also discussing conditional image generation methods such as Conditional Generative Adversarial Networks (CGAN) and Stacked Generative Adversarial Network (StackGAN). Furthermore, it explores deep learning-based image style migration methods, including both image iteration-based and model iteration-based approaches. The possibilities and opportunities for the advancement and use of these techniques in the field of picture production are highlighted in the review's conclusion.
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Fekri, Mohammad Navid, Ananda Mohon Ghosh, and Katarina Grolinger. "Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks." Energies 13, no. 1 (2019): 130. http://dx.doi.org/10.3390/en13010130.
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The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.
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Dong, Chi, Xianhai Pang, Shijie Lu, Jinjian Zhao, Zhaochen Liu, and Jun Xie. "Partial Discharge Data Augmentation and Pattern Recognition for Unbalanced and Small Sample Scenarios." Journal of Physics: Conference Series 2477, no. 1 (2023): 012078. http://dx.doi.org/10.1088/1742-6596/2477/1/012078.
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Abstract To improve the accuracy of partial discharge pattern recognition under unbalanced and small sample conditions, a method of partial discharge data augmentation and pattern recognition using the generative adversarial network embedded deep auto-encoder (DAE-GAN) is proposed. First, deep Auto-encoder (DAE) is embedded into a Generative Adversarial Network (GAN), and DAE is used to guide the generation process to improve the authenticity of generated samples. Second, complement samples of PD samples are added to the training process of the Generative Adversarial Network to solve the problem of small PD samples. Finally, extended equalization training samples are used to fine-tune the discriminator of the model to realize PD pattern recognition. DAE-GAN is used for data augmentation and pattern recognition of partial discharge experimental signals. The results show that, compared with other algorithms, the authenticity and probability distribution fitting accuracy of partial discharge samples generated by DAE-GAN are higher and the accuracy of partial discharge recognition is improved by 8.24% after data augmentation.
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Sumit, Gunjate, Nakhate Tushar, Kshirsagar Tushar, and Sapat Yash. "Sketch to Image using GAN." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 772–77. https://doi.org/10.5281/zenodo.7588232.
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With the development of the modern age and its technologies, people are discovering ways to improve, streamline, and de-stress their lives. A difficult issue in computer vision and graphics is the creation of realistic visuals from hand-drawn sketches. There are numerous uses for the technique of creating facial sketches from real images and its inverse. Due to the differences between a photo and a sketch, photo/sketch synthesis is still a difficult problem to solve. Existing methods either require precise edge maps or rely on retrieving previously taken pictures. In order to get around the shortcomings of current systems, the system proposed in this paper uses generative adversarial networks. A type of machine learning method is called a generative adversarial network (GAN). This algorithm pits two or more neural networks against one another inthe context of a zero-sum game. Here, we provide a generative adversarial network (GAN) method for creating convincing images. Recent GAN-based techniques for sketch-to-image translation issues have produced promising results. Our technology produces photos that are more lifelike than those made by other techniques. According to experimental findings, our technology can produce photographs that are both aesthetically pleasing and identity-Preserving using a variety of difficult data sets.
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Rui Yang, Rui Yang, Tian-Jie Cao Rui Yang, Xiu-Qing Chen Tian-Jie Cao, Feng-rong Zhang Xiu-Qing Chen, and Yun-Yan Qi Feng-rong Zhang. "An Ensemble Denoiser Based on Generative Adversarial Networks to Eliminate Adversarial Perturbations." 電腦學刊 32, no. 5 (2021): 055–75. http://dx.doi.org/10.53106/199115992021103205005.
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Deep neural networks (DNNs) have been applied in various machine learning tasks with the success of deep learning technologies. However, they are surprisingly vulnerable to adversarial examples, which can easily fool deep neural networks. Due to this drawback of deep neural networks, numerous methods have been proposed to eliminate the effect of adversarial examples. Although they do play a significant role in protecting deep neural networks, most of them all have one flaw in common. They are only effective for certain types of adversarial examples. This paper proposes an ensemble denoiser based on generative adversarial networks (GANs) to protect deep neural networks. This proposed method aims to remove the effect of multiple types of adversarial examples before they are fed into deep neural networks. Therefore, it is model-independent and cannot modify deep neural networks&rsquo; parameters. We employ a generative adversarial network for this proposed method to learn multiple mappings between adversarial examples and benign examples. Each mapping behaves differently for different types of adversarial examples. Therefore, we integrate these mappings as the ultimate method to defend against multiple types of adversarial examples. Experiments are conducted on the MNIST and CIFAR10 datasets. We compare this proposed method with several existing excellent methods. Results show that this proposed method achieves better performance than other methods when defending against multiple types of adversarial examples. The code is available at <a href="https://github.com/Afreadyang/ensemble-ape-gan">https://github.com/Afreadyang/ensemble-ape-gan</a>
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Zeng, Hongzhi. "Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models." ITM Web of Conferences 70 (2025): 03019. https://doi.org/10.1051/itmconf/20257003019.
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The vast application of artificial intelligence in numerous fields—image generation being one of them—has been made possible by the quick development of deep learning. Generative Adversarial Networks (GAN) can generate high-quality images through an adversarial training mechanism. The use and performance of GAN and its conditional variation, CGAN, in the field of handwritten digital image generation, are thoroughly examined in this research. The basic GAN and CGAN models, based on the PyTorch deep learning framework and the Modified National Institute of Standards and Technology (MNIST) dataset, are applied to generate handwritten digital images respectively. To assess and compare the variations between the two models concerning the fineness of image generation, the loss changes, and other relevant factors, the generation outcomes and the loss changes that occur during the training phase are documented. The experimental results demonstrate that, compared with the basic GAN, CGAN exhibits notable advantages in terms of image quality stability, the avoidance of model collapse, and the control of image categories. Furthermore, an investigation of other cutting-edge generating models indicates that there is still room for optimization in the CGAN network structure to improve its performance for increasingly intricate generative tasks.
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Hazra, Debapriya, and Yung-Cheol Byun. "SynSigGAN: Generative Adversarial Networks for Synthetic Biomedical Signal Generation." Biology 9, no. 12 (2020): 441. http://dx.doi.org/10.3390/biology9120441.
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Automating medical diagnosis and training medical students with real-life situations requires the accumulation of large dataset variants covering all aspects of a patient’s condition. For preventing the misuse of patient’s private information, datasets are not always publicly available. There is a need to generate synthetic data that can be trained for the advancement of public healthcare without intruding on patient’s confidentiality. Currently, rules for generating synthetic data are predefined and they require expert intervention, which limits the types and amount of synthetic data. In this paper, we propose a novel generative adversarial networks (GAN) model, named SynSigGAN, for automating the generation of any kind of synthetic biomedical signals. We have used bidirectional grid long short-term memory for the generator network and convolutional neural network for the discriminator network of the GAN model. Our model can be applied in order to create new biomedical synthetic signals while using a small size of the original signal dataset. We have experimented with our model for generating synthetic signals for four kinds of biomedical signals (electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), photoplethysmography (PPG)). The performance of our model is superior wheen compared to other traditional models and GAN models, as depicted by the evaluation metric. Synthetic biomedical signals generated by our approach have been tested while using other models that could classify each signal significantly with high accuracy.
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Hemberg, Erik, Jamal Toutouh, Abdullah Al-Dujaili, Tom Schmiedlechner, and Una-May O’reilly. "Spatial Coevolution for Generative Adversarial Network Training." ACM Transactions on Evolutionary Learning and Optimization 1, no. 2 (2021): 1–28. http://dx.doi.org/10.1145/3458845.
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Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, as well as in combination, play critical roles. Lipizzaner succumbs less frequently to critical collapses and, as a side benefit, demonstrates improved performance. In addition, we show a GAN-training feature of Lipizzaner: the ability to train simultaneously with different loss functions in the gradient descent parameter learning framework of each GAN at each cell. We use an image generation problem to show that different loss function combinations result in models with better accuracy and more diversity in comparison to other existing evolutionary GAN models. Finally, Lipizzaner with multiple loss function options promotes the best model diversity while requiring a large grid size for adequate accuracy.
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Alanazi, Meshari Huwaytim. "G-GANS for Adaptive Learning in Dynamic Network Slices." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14327–41. http://dx.doi.org/10.48084/etasr.7046.
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This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.
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Kumar, Chandan, Amzad Choudhary, Gurpreet Singh, and Ms Deepti Gupta. "Enhanced Super-Resolution Using GAN." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2077–80. http://dx.doi.org/10.22214/ijraset.2022.42718.
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Abstract: Super-resolution reconstruction is an increasingly important area in computer vision. To eliminate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results. besides the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks. However, the hallucinated details are often accompanied with unpleasant artifacts. This paper presented ESRGAN model which was also based on generative adversarial networks. To further enhance the visual quality, we thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN.
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Kolesnikov, Vladimir Dmitrievich, and Petr Stepanovich Kabalyants. "Application of recurrent neural network models for generating text data of specifications of assembly drawings." Research Result. Information technologies 9, no. 4 (2024): 51–57. https://doi.org/10.18413/2518-1092-2024-9-4-0-6.
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The article explores the capabilities of various models of recurrent neural networks in text data generation. Specifically, classic recurrent network (RNN), long short-term memory network (LSTM) and generative adversarial network (GAN) models are considered in the context of the problem of generating specification text for assembly drawings according to the format approved by government standard. To train the models, a data set in Russian language was used, expanded with additional records simulating input data, consisting of the drawing parts, and expected text of the specifications. The input data set for the study was divided into four groups of equal size, depending on three main factors: amount of input parts, their repeatability and grammatical complexity. It is concluded that for all four groups of input data generative adversarial networks (GANs) have the maximum ratio of error-free responses to all responses generated by model, followed by LSTMs and, lastly – RNNs. As a result, it is planned to use GAN-based models in future researches on specification text data generation.
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Chakraborty, Sujoy. "Camera Fingerprint estimation with a Generative Adversarial Network (GAN)." Electronic Imaging 2021, no. 4 (2021): 336–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-336.
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For forensic analysis of digital images or videos, the PRNU or camera fingerprint is the most important characteristics, for source attribution and manipulation localization. Typically, a good estimate of the PRNU is obtained by computing its Maximum Likelihood estimate from noise residuals of a large number of flatfield images captured by the camera. In this paper, we propose a novel approach of estimating the fingerprint of a camera, with a Generative Adversarial Network (GAN). The idea is to let the Generator network learn a distribution, from which PRNU samples will be drawn after training of the two adversarial networks. Experimental results indicate that the GAN-generated PRNU yields state-of-the-art camera identification and manipulation localization results.