Toward Hidden Data Detection via Local Features Optimization in Spatial Domain Images

Ntivuguruzwa Jean De La Croix, Tohari Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Citations (Scopus)

Abstract

Technology advancements made machine learning algorithms crucial to solving complex problems. Deep learning, a machine learning paradigm to design convolutional neural networks (CNNs), achieves promising performance in detecting confidential data, known as steganalysis. However, the existing steganalysis CNNs have not achieved optimal performance detecting accuracy and network stability. In this research, we propose a new approach within CNN to improve the secret data detection accuracy by optimizing the local features in the feature extraction stage of the spatial domain images. The performance is evaluated using the Break Our Steganographic System Base (BOSSBase) dataset with two standard adaptive steganography algorithms employing low payload capacities of 0.2 and 0.4 bits per pixel. The experimental results outperform the results of the previously published works in accuracy and network stability. The detection accuracy is improved in a range between 2.1% to 3.6%.

Original languageEnglish
Title of host publication2023 Conference on Information Communications Technology and Society, ICTAS 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665489300
DOIs
Publication statusPublished - 2023
Event7th Conference on Information Communications Technology and Society, ICTAS 2023 - Durban, South Africa
Duration: 8 Mar 20239 Mar 2023

Publication series

Name2023 Conference on Information Communications Technology and Society, ICTAS 2023 - Proceedings

Conference

Conference7th Conference on Information Communications Technology and Society, ICTAS 2023
Country/TerritorySouth Africa
CityDurban
Period8/03/239/03/23

Keywords

  • Convolutional neural network
  • Data hiding
  • Network infrastructure
  • Spatial domain images
  • Steganalysis

Fingerprint

Dive into the research topics of 'Toward Hidden Data Detection via Local Features Optimization in Spatial Domain Images'. Together they form a unique fingerprint.

Cite this