A Common Steganalysis Method of Low Embedding Rate Steganography in Compressed Speech Based on Hierarchy Feature Extraction and Fusion

F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

Security and Communication Networks ◽

10.1155/2021/1627486 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Chuanpeng Guo ◽

Wei Yang ◽

Mengxia Shuai ◽

Liusheng Huang

Keyword(s):

Linear Prediction ◽

State Of The Art ◽

Communication Security ◽

Great Success ◽

Attention Network ◽

Compressed Speech ◽

Different Types ◽

Comprehensive Comparison ◽

Embedding Rate ◽

Memory Characteristics

Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rate samples. To deal with the challenge, we use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet—a four-step strategy: embedding, encoding, attention, and classification for quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. Among them, embedding converts codewords into high-density numerical vectors, encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors, and attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make a comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.

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General Steganalysis Method of Compressed Speech Under Different Standards

Computers Materials & Continua ◽

10.32604/cmc.2021.016635 ◽

2021 ◽

Vol 68 (2) ◽

pp. 1565-1574

Author(s):

Peng Liu ◽

Songbin Li ◽

Qiandong Yan ◽

Jingang Wang ◽

Cheng Zhang

Keyword(s):

Compressed Speech ◽

Steganalysis Method

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A Universal Steganalysis Algorithm for JPEG Image Based on Selective SVMs Ensemble

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1548 ◽

2012 ◽

Vol 532-533 ◽

pp. 1548-1552 ◽

Cited By ~ 1

Author(s):

Da Ya Chen ◽

Shang Ping Zhong

Keyword(s):

Detection Rate ◽

Ensemble Classifier ◽

Support Vector ◽

Jpeg Image ◽

Different Types ◽

Universal Steganalysis ◽

Ensemble Algorithm ◽

Embedding Rate ◽

Steganalysis Method ◽

Better Than

Universal steganalysis include feature extraction and steganalyzer design. Most universal steganalysis use Support Vector Machine (SVM) as steganalyzer. However, most SVM-based universal steganalysis are not to be very much effective at lower embedding rates. The reason why selective SVMs ensemble improve the generalization ability was analyzed, and an algorithm to select a part of individual SVMs according to their difference to build the ensemble classifier was proposed, which based on the selected ensemble theory-Many could be better than all. In this paper, the selective SVMs ensemble algorithm was used to construct a strong steganalyzer to improve the performance of steganographic detection. The twenty five experiments on the benchmark with 2000 different types of images show that: for popular steganography methods, and under different conditions of embedding rate, the average detection rate of proposed steganalysis method outperforms the maximum average detection rate for the steganalysis method based on single SVM with improving by 3.05%-12.05%; and for the steganalysis method based on BaggingSVM with improving by 0.2%-1.3%.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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