This paper presents an effective steganalytic scheme based on CNN for detecting MP3 steganography in the entropy code domain. These steganographic methods hide secret messages into the compressed audio stream through ...
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ISBN:
(纸本)9781450356251
This paper presents an effective steganalytic scheme based on CNN for detecting MP3 steganography in the entropy code domain. These steganographic methods hide secret messages into the compressed audio stream through Huffman code substitution, which usually achieve high capacity, good security and low computational complexity. First, unlike most previous CNN based steganalytic methods, the quantified modified DCT (QMDCT) coefficients matrix is selected as the input data of the proposed network. Second, a high pass filter is used to extract the residual signal, and suppress the content itself, so that the network is more sensitive to the subtle alteration introduced by the data hiding methods. Third, the 1 x 1 convolutional kernel and the batch normalization layer are applied to decrease the danger of overfitting and accelerate the convergence of the back- propagation. In addition, the performance of the network is optimized via fine- tuning the architecture. The experiments demonstrate that the proposed CNN performs far better than the traditional handcrafted features. In particular, the network has a good performance for the detection of an adaptive MP3 steganography algorithm, equal length entropy codes substitution (EECS) algorithm which is hard to detect through conventional handcrafted features. The network can be applied to various bitrates and relative payloads seamlessly. Last but not the least, a sliding window method is proposed to steganalyze audios of arbitrary size.
In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between ...
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In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression, with a lower complexity.
Background: Surface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digit...
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Background: Surface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work. Methods: This paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape. Results: The proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown. Conclusions: The decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data com
RS (Reed Solomon) codes have strong error correction capability. The reliability of information transmission can be improved by RS codes. It is widely used in modern communication. The blind recognition of RS codes is...
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ISBN:
(纸本)9781479958368
RS (Reed Solomon) codes have strong error correction capability. The reliability of information transmission can be improved by RS codes. It is widely used in modern communication. The blind recognition of RS codes is particularly important in the field of information interception and intelligent communication. In order to solve the blind recognition problem of the high rate RS codes, this paper presents a blind recognition method of RS codes based on Galois field columns Gaussian elimination. Firstly, code length and symbolic number are recognized by using the difference function of the matrix rank. Then the primitive proved the polynomials corresponding to the number of symbol at this time are traversed. And the matrix is eliminated with columns in Galois field. By using the difference of entropy, the primitive polynomial is identified. Finally, as the code word polynomial roots are found, the continuous roots are the roots of the generator polynomial. code length, the primitive polynomial and the generator polynomial are identified by the method. The tedious process of Galois field Fourier transform is avoided. Simulation results show that the recognition probability is higher than 90% at an error code rate of 3×10~(-3).
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