A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
- Title
- A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks
- Creator
- Gurunath R.; Alahmadi A.H.; Samanta D.; Khan M.Z.; Alahmadi A.
- Description
- Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study's shortcomings, which may be the subject of future research. 2013 IEEE.
- Source
- IEEE Access, Vol-9, pp. 120869-120879.
- Date
- 2021-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Artificial intelligence; linguistic steganography; LSTM; Markov chain model; natural language processing; NLP; recurrent neural networks; RNN; statistical language model; steganography
- Coverage
- Gurunath R., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Alahmadi A.H., Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia; Samanta D., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Khan M.Z., Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia; Alahmadi A., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 21693536
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Gurunath R.; Alahmadi A.H.; Samanta D.; Khan M.Z.; Alahmadi A., “A Novel Approach for Linguistic Steganography Evaluation Based on Artificial Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/16008.