Insights into Artificial Neural Network techniques, and its Application in Steganography
- Title
- Insights into Artificial Neural Network techniques, and its Application in Steganography
- Creator
- Gurunath R.; Samanta D.
- Description
- Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved.
- Source
- Journal of Physics: Conference Series, Vol-2089, No. 1
- Date
- 2021-01-01
- Publisher
- IOP Publishing Ltd
- Subject
- Convolution Neural Networks; Deep Steganography; Feed forward; LSTM; Optimization Algorithms; Recurrent Neural network
- Coverage
- Gurunath R., Department of Computer Science, CHRIST University, Bangalore, India; Samanta D., Department of Computer Science, CHRIST University, Bangalore, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 17426588
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Gurunath R.; Samanta D., “Insights into Artificial Neural Network techniques, and its Application in Steganography,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20471.