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  • Open Access

    ARTICLE

    Contourlet and Gould Transforms for Hybrid Image Watermarking in RGB Color Images

    Reena Thomas1,*, M. Sucharitha2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 879-889, 2022, DOI:10.32604/iasc.2022.024070 - 08 February 2022

    Abstract The major intention of this work is to introduce a novel hybrid image watermarking technique for RGB color images. This hybrid watermarking algorithm uses two transforms such as Contourlet and Gould transform. The Contourlet transform is used as first stage while the Gould transform is used as second stage. In the watermark embedding phase, the R, G and B channels are transformed using Contourlet transform. The bandpass directional sub band coefficients of Contourlet transformed image are then divided into sub-blocks. The sub-blocks are then transformed using Gould transform and the watermark information is embedded on More >

  • Open Access

    ARTICLE

    Steganalysis of Low Embedding Rate CNV-QIM in Speech

    Wanxia Yang*, Miaoqi Li, Beibei Zhou, Yan Liu, Kenan Liu, Zhiyu Hu

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 623-637, 2021, DOI:10.32604/cmes.2021.015629 - 22 July 2021

    Abstract To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior More >

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