A preprocessing by using multiple steganography for intentional image downsampling on CNN-based steganalysis

Hiroya Kato, Kyohei Osuge, Shuichiro Haruta, Iwao Sasase

研究成果: Article査読

抄録

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, the detection performance of CNN is degraded when an image is intentionally resized by the nearest-neighbor interpolation before steganography. This is because spatial frequency in a resized image gets high, which disturbs the training. In order to overcome this shortcoming, in this article, we propose a preprocessing by using multiple steganography for intentional image downsampling on CNN-based steganalysis. In the proposed preprocessing, steganographic signals are additionally embedded into both resized original images and resized steganographic ones with the same embedding key since difference of spatial frequencies between them gets obvious, which helps CNN learn features. The reason why the difference gets obvious is that steganographic signals tend to be continuously embedded into same pixels in resized images when they are additionally embedded. Thus, by training resized images after the proposed preprocessing, the detection performance can be improved. Since the proposed preprocessing is very simple, it does not greatly increase the training time of CNN. Our evaluation shows accuracy in a model with the proposed preprocessing is up to 34.8% higher than that in the conventional model when the same steganography is additionally embedded. Besides, we also show that the proposed preprocessing yields up to 23.1% higher accuracy compared with the conventional one even when another steganography is additionally embedded.

本文言語English
ページ(範囲)195578-195593
ページ数16
ジャーナルIEEE Access
8
DOI
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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