Detection of malicious spatial-domain steganography over noisy channels

Swaroop Shankar Prasad, Ofer Hadar, Ilia Polian

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Steganographic channels can be abused for malicious purposes, thus raising the need to detect malicious embedded steganographic information (steganalysis). This chapter will cover the little-studied problem of steganography and steganalysis over a noisy channel, providing a detailed modeling for the special case of spatial-domain image steganography. It will approach these issues from both a theoretical and a practical point of view. After a description of spatial-domain image steganography, the impact of Gaussian noise and packet loss on the steganographic channel will be discussed. Characterization of the substitution-insertion-deletion (SID) channel parameters will be performed through experiments on a large number of images from the ALASKA database. Finally, a steganalysis technique for error-affected spatial-domain image steganography using a convolutional neural network (CNN) will be introduced, studying the relationship between different types and levels of distortions and the accuracy of malicious image detection.

Original languageEnglish
Title of host publicationMultidisciplinary Approach to Modern Digital Steganography
PublisherIGI Global
Pages125-145
Number of pages21
ISBN (Electronic)9781799871620
ISBN (Print)9781799871606
DOIs
StatePublished - 4 Jun 2021

ASJC Scopus subject areas

  • General Computer Science

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