Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Abstract

Digital steganography can be difficult to detect and as such is an ideal way of engaging in covert communications across the Internet. This research paper is a work-in-progress report on instances of steganography that were identified on websites on the Internet including some from the DarkWeb using the application of new methods of deep learning algorithms. This approach to the identification of Least Significant Bit (LSB) Steganography using Convolutional Neural Networks (CNN) has demonstrated some efficiency for image classification. The CNN algorithm was trained using datasets of images with known steganography and then applied to datasets with images to identify concealed data. The algorithm was trained using 5000 clean images and 5000 Steganography images. With the correct configurations made to the deep learning algorithms, positive results were obtained demonstrating a greater speed, accuracy and fewer false positives than the current steganalysis tools.

DOI

https://doi.org/10.34190/EWS.20.504


Share

COinS