Games of GANs: game-theoretical models for generative adversarial networks

Moghaddam, Monireh Mohebbi; Boroomand, Bahar; Jalali, Mohammad; Zareian, Arman; Daeijavad, Alireza; Manshaei, Mohammad Hossein; Krunz, Marwan

doi:10.1007/s10462-023-10395-6

Cited by 17 publications

(4 citation statements)

References 87 publications

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“…A GAN consists of a generator

neural network and a discriminator

neural network. The aim is for the generator to create synthetic data that are indistinguishable from real data, while the discriminator aims to correctly differentiate between real and synthetic data [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

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“…A GAN consists of a generator

neural network and a discriminator

Section: Methodsmentioning

confidence: 99%

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Gunduz,

Das

2024

Sensors

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“…Font Generation Process: To generate realistic synthetic images, it is important to use fonts that are representative of the languages and writing systems. There are several methods for creating fonts, including the use of generative adversarial networks (GANs) [12,13] or variational autoencoders (VAEs) [14] or simply choosing fonts from libraries that already exist.…”

Section: Steps Of Training Dataset Generationmentioning

confidence: 99%

Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods

Biró,

Szilágyi,

Szilágyi

2023

Applied Sciences

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In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual understanding, domain-specific adaptation, robustness and noise tolerance, and scalability and extensibility. The approach aims to leverage techniques like convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, and single visual models for scene text recognition. While focusing on English, Hungarian, and Japanese as representative languages, the proposed methodology can be extended to any existing or even synthesized languages. The development of accurate, efficient, and versatile OCR systems is at the core of this research, offering societal benefits by bridging global communication gaps, ensuring reliability in diverse environments, and demonstrating the adaptability of AI to evolving needs. This work not only mirrors the state of the art in the field but also paves new paths for future innovation, accentuating the importance of sustained research in advancing AI’s potential to shape societal development.

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“…In particular, a concept called “game blending” was adopted by Gow and Coreneli to establish a framework that effectively produces new games from multiple existing games [ 25 ]; while the Long Short-Term Memory (LSTM) technique has also been applied to blend computer game levels based on Mario and Kid Icarus, then combine with the Variational AutoEncoder (VAE) model to generate more controllable game levels [ 26 , 27 ]. In recent years, Generative Adversarial Network (GAN) models have become popular, and have been incorporated into the framework of generating game levels and images under specific conditions and settings [ 28 , 29 ]. These black-box models allow users to design and generate levels in an automatic manner, thus Schrum et al [ 30 ] utilized such unique features to develop a latent model-based game designing tool; while Torrado et al [ 31 ] investigated the conditional GAN and established a new GAN-based architecture called “Conditional Embedding Self-Attention GAN”, then equipped it with the bootstrapping mechanism for the purpose of generating Role-Playing Games (RPG) levels.…”

Section: Introductionmentioning

confidence: 99%

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

Mak

Han

Yin

2023

Sensors

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In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate new game levels based on two well-known game settings; and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed; then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

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Games of GANs: game-theoretical models for generative adversarial networks

Cited by 17 publications

References 87 publications

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns

Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

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