Image Steganalysis Based on HPF Neural Network

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.

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Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain

PeerJ Computer Science ◽

10.7717/peerj-cs.451 ◽

2021 ◽

Vol 7 ◽

pp. e451

Author(s):

Reinel Tabares-Soto ◽

Harold Brayan Arteaga-Arteaga ◽

Alejandro Mora-Rubio ◽

Mario Alejandro Bravo-Ortíz ◽

Daniel Arias-Garzón ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Detection Accuracy ◽

Network Architectures ◽

Single Model ◽

Image Steganalysis ◽

Training Time ◽

Absolute Value ◽

Learning Techniques ◽

Improve Accuracy

In recent years, Deep Learning techniques applied to steganalysis have surpassed the traditional two-stage approach by unifying feature extraction and classification in a single model, the Convolutional Neural Network (CNN). Several CNN architectures have been proposed to solve this task, improving steganographic images’ detection accuracy, but it is unclear which computational elements are relevant. Here we present a strategy to improve accuracy, convergence, and stability during training. The strategy involves a preprocessing stage with Spatial Rich Models filters, Spatial Dropout, Absolute Value layer, and Batch Normalization. Using the strategy improves the performance of three steganalysis CNNs and two image classification CNNs by enhancing the accuracy from 2% up to 10% while reducing the training time to less than 6 h and improving the networks’ stability.

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CNN-Based Ternary Classification for Image Steganalysis

Electronics ◽

10.3390/electronics8111225 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1225 ◽

Cited By ~ 3

Author(s):

Sanghoon Kang ◽

Hanhoon Park ◽

Jong-Il Park

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Steganalysis ◽

Embedding Methods

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%.

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