A Preprocessing Methodology by Using Additional Steganography on CNN-based Steganalysis

Hiroya Kato, Kyohei Osuge, Shuichiro Haruta, Iwao Sasase

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There exists a need of 'image steganalysis' which reveals whether steganographic signals are embedded in an image to improve information security. Among various steganalysis, Convolutional Neural Networks (CNN) based steganalysis is promising since it can automatically learn the features of diverse steganographic algorithms. However, we discover the detection performance of CNN is degraded when an image is intentionally reduced by the nearest-neighbor interpolation before steganography. This is because spatial frequency in a reduced image gets high, which disturbs the training. In order to overcome this shortcoming, in this paper, we propose a preprocessing methodology by using additional steganography on CNN-based steganalysis. In the proposed preprocessing, steganographic signals are additionally embedded into both reduced original images and reduced steganographic ones since a difference of spatial frequencies between them gets obvious, which helps CNN learn features. Whenever reduced images are trained in CNN or inspected whether they are steganographic ones or not, steganography is applied to them once by the proposed preprocessing. Thus, an image is regarded as a steganographic one if the trained model judges steganography is applied to it twice; otherwise it is an original one. Since the proposed methodology is very simple, its computational cost is low. Our evaluation shows accuracy in a model with the proposed preprocessing is 10.6% higher than that in the conventional one. Besides, even in the situation where another steganography is additionally embedded, the proposed preprocessing yields 7% higher accuracy compared with the conventional one.

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728182988
DOIs
Publication statusPublished - 2020 Dec
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Publication series

Name2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
CountryTaiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

Keywords

  • Convolutional Neural Networks
  • Image Downsampling
  • Steganalysis

ASJC Scopus subject areas

  • Media Technology
  • Modelling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Safety, Risk, Reliability and Quality

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