VideoTrain++: GAN-based adaptive framework for synthetic video traffic generation

Kattadige, Chamara; Muramudalige, Shashika R.; Jourjon, Guillaume; Jayasumana, Anura P.; Thilakarathna, Kanchana

doi:10.1016/j.comnet.2022.108785

Cited by 15 publications

(2 citation statements)

References 25 publications

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“…In unsupervised settings, the models are trained without access to the labels. For the W-VAE, the same model architecture is reused since only the loss (negative reward) function is changed to (9), and the same number of parameters. However, without label information the trained model's performance relies more on the initialization point.…”

Section: B Unsupervised Device Identificationmentioning

confidence: 99%

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Huang¹,

Dahanayaka²,

Thilakarathna³

et al. 2023

Preprint

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Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multiview machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our informationtheoretic approach can be extended to supervised and semisupervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings.

show abstract

Section: B Unsupervised Device Identificationmentioning

confidence: 99%

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Huang¹,

Dahanayaka²,

Thilakarathna³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…4. Generative Adversarial Networks (GAN) (Madarasingha et al, 2022): GAN consists of a generator and a discriminator, employing adversarial learning to generate realistic data. The generator aims to create samples realistic enough, while the discriminator endeavors to differentiate between real and generated data.…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

Zhang,

Qu,

Long

et al. 2024

Journal of Organizational and End User Computing

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With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.

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The Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced Learning Experiences

Kumar,

Kait,

Ankita

et al. 2024

Lecture Notes in Electrical Engineering

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VideoTrain++: GAN-based adaptive framework for synthetic video traffic generation

Cited by 15 publications

References 25 publications

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

The Role of Generative Artificial Intelligence (GAI) in Education: A Detailed Review for Enhanced Learning Experiences

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