Digital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography method that can hide plaintext payloads on digital halftone images. The proposed method distributes the secret message over multiple output copies and scatters parts of the message randomly within each output copy for increased security. A payload extraction algorithm, where plain carrier is not required, is implemented and presented as well. Results gained from conducted objective and subjective tests prove that the proposed steganography method is secure and can hide large payloads.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455272 | PMC |
| http://dx.doi.org/10.7717/peerj-cs.1062 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Model and Quantitative Framework for Evaluating Iterative Steganography.
Entropy (Basel)
December 2024
Institute of Computer and Information Systems, Faculty of Cybernetics, Military University of Technology, 00-908 Warszawa, Poland.
This study presents a detailed characterization of iterative steganography, a unique class of information-hiding techniques, and proposes a formal mathematical model for their description. A novel quantitative measure, the Incremental Information Function (IIF), is introduced to evaluate the process of information gain in iterative steganographic methods. The IIF offers a comprehensive framework for analyzing the step-by-step process of embedding information into a cover medium, focusing on the cumulative effects of each iteration in the encoding and decoding cycles.
View Article and Find Full Text PDFA novel and efficient digital image steganography technique using least significant bit substitution.
Sci Rep
January 2025
Universidad Europea del Atlántico. Isabel Torres 21, Santander, 39011, Spain.
Steganography is used to hide sensitive types of data including images, audio, text, and videos in an invisible way so that no one can detect it. Image-based steganography is a technique that uses images as a cover media for hiding and transmitting sensitive information over the internet. However, image-based steganography is a challenging task due to transparency, security, computational efficiency, tamper protection, payload, etc.
View Article and Find Full Text PDFSecure cloud computing: leveraging GNN and leader K-means for intrusion detection optimization.
Sci Rep
December 2024
Department of Computer Science , Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Over the past two decades, cloud computing has experienced exponential growth, becoming a critical resource for organizations and individuals alike. However, this rapid adoption has introduced significant security challenges, particularly in intrusion detection, where traditional systems often struggle with low detection accuracy and high processing times. To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm.
View Article and Find Full Text PDFEnhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography.
Sensors (Basel)
December 2024
Institute of Security and Computer Science, University of the National Education Commission, 30-084 Krakow, Poland.
Article Synopsis
- The paper explores the use of Convolutional Neural Networks (CNNs) for detecting image steganography, focusing on SRNet's performance against different steganography techniques.
- Initial tests revealed that SRNet struggled with Spread Spectrum Image Steganography (SSIS), leading to fine-tuning for better detection of SSIS, which improved results but slightly weakened performance on other methods.
- The study emphasizes the need for a balance in model adaptation and suggests future research on multi-task learning and other machine learning strategies to enhance the effectiveness of steganalysis models.
Sterilization of image steganography using self-supervised convolutional neural network.
PeerJ Comput Sci
September 2024
College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.
Background: With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!