HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks

Rohit Gandikota; Deepak Mishra

doi:10.1007/978-3-031-31407-0_3

ScienceGate Book Chapters

BOOK-CHAPTER

HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks

Rohit Gandikota Deepak Mishra

Year: 2023 Communications in computer and information science Pages: 29-43 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-31407-0_3

Get Full-Text PDF Get Analytical Report

Keywords:

Autoencoder Computer science Artificial intelligence Noise (video) Image (mathematics) Sensitivity (control systems) Container (type theory) Pattern recognition (psychology) Extractor Computer vision Deep learning

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.12

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Generative Adversarial Networks and Image Synthesis

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks

Metrics

Topics

Related Documents

GAN-VAE: Elevate Generative Ineffective Image Through Variational Autoencoder

Comparative Analysis of Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) Algorithms for image

A Variational Autoencoder—General Adversarial Networks (VAE-GAN) Based Model for Ligand Designing

AD-VAE: Adversarial Disentangling Variational Autoencoder

AEGANAuth: Autoencoder GAN-Based Continuous Authentication With Conditional Variational Autoencoder Generative Adversarial Network