Next Article in Journal
Software-Based Adaptive Protection Control against Load Mismatch for a Mobile Power Amplifier Module
Next Article in Special Issue
An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning
Previous Article in Journal
Designing a Water-Immersed Rectangular Horn Antenna for Generating Underwater OAM Waves
Previous Article in Special Issue
False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning
Open AccessArticle

CNN-Based Ternary Classification for Image Steganalysis

1
Department of Electronic Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Korea
2
Department of Computer Science, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1225; https://doi.org/10.3390/electronics8111225
Received: 11 September 2019 / Revised: 18 October 2019 / Accepted: 23 October 2019 / Published: 26 October 2019
This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have very similar message embedding methods in terms of measuring and minimizing the degree of distortion of images caused by message embedding. This similarity between WOW and UNIWARD makes it difficult to distinguish between both algorithms even in a CNN-based classifier. Our experiments particularly show that WOW and UNIWARD cannot be distinguished by simply combining binary CNN-based classifiers learned to separately identify both algorithms. Therefore, to identify and classify WOW and UNIWARD, WOW and UNIWARD must be learned at the same time using a single CNN-based classifier designed for ternary classification. This study proposes a method for ternary classification that learns and classifies cover, WOW stego, and UNIWARD stego images using a single CNN-based classifier. A CNN structure and a preprocessing filter are also proposed to effectively classify/identify WOW and UNIWARD. Experiments using BOSSBase 1.01 database images confirmed that the proposed method could make a ternary classification with an accuracy of approximately 72%. View Full-Text
Keywords: image steganalysis; WOW; UNIWARD; ternary classification; convolutional neural network (CNN) image steganalysis; WOW; UNIWARD; ternary classification; convolutional neural network (CNN)
Show Figures

Figure 1

MDPI and ACS Style

Kang, S.; Park, H.; Park, J.-I. CNN-Based Ternary Classification for Image Steganalysis. Electronics 2019, 8, 1225.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop