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Search Results (19)
  • Open Access


    Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

    Arslan Akram1,2, Imran Khan1, Javed Rashid2,3, Mubbashar Saddique4,*, Muhammad Idrees4, Yazeed Yasin Ghadi5, Abdulmohsen Algarni6

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1311-1328, 2024, DOI:10.32604/cmc.2023.040512

    Abstract Algorithms for steganography are methods of hiding data transfers in media files. Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information, and these methods have made it feasible to handle a wide range of problems associated with image analysis. Images with little information or low payload are used by information embedding methods, but the goal of all contemporary research is to employ high-payload images for classification. To address the need for both low- and high-payload images, this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to… More >

  • Open Access


    Image Steganalysis Based on Deep Content Features Clustering

    Chengyu Mo1,2, Fenlin Liu1,2, Ma Zhu1,2,*, Gengcong Yan3, Baojun Qi1,2, Chunfang Yang1,2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540

    Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.… More >

  • Open Access


    A Deep Learning Driven Feature Based Steganalysis Approach

    Yuchen Li1, Baohong Ling1,2,*, Donghui Hu1, Shuli Zheng1, Guoan Zhang3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2213-2225, 2023, DOI:10.32604/iasc.2023.029983

    Abstract The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic algorithms can obtain diverse features… More >

  • Open Access


    A General Linguistic Steganalysis Framework Using Multi-Task Learning

    Lingyun Xiang1,*, Rong Wang1, Yuhang Liu1, Yangfan Liu1, Lina Tan2,3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2383-2399, 2023, DOI:10.32604/csse.2023.037067

    Abstract Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a… More >

  • Open Access


    MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

    Rukiye Karakis1,2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881

    Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance… More >

  • Open Access


    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

    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 detection accuracy than state-of-the-art methods… More >

  • Open Access


    General Steganalysis Method of Compressed Speech Under Different Standards

    Peng Liu1, Songbin Li1,*, Qiandong Yan1, Jingang Wang1, Cheng Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1565-1574, 2021, DOI:10.32604/cmc.2021.016635

    Abstract Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. Most of the current steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for a specific coding standard or category of steganography methods, and thus lack generalization capability. In this paper, a general steganalysis method for detecting steganographies in low-bit-rate compressed speech under different standards is proposed. First, the code-element matrices corresponding to different coding standards are concatenated to obtain a synthetic code-element matrix, which will be mapped into an intermediate feature representation by utilizing the pre-trained dictionaries. Then, bidirectional long short-term memory… More >

  • Open Access


    Image Steganography in Spatial Domain: Current Status, Techniques, and Trends

    Adeeb M. Alhomoud*

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 69-88, 2021, DOI:10.32604/iasc.2021.014773

    Abstract This research article provides an up-to-date review of spatial-domain steganography. Maintaining the communication as secure as possible when transmitting secret data through any available communication channels is the target of steganography. Currently, image steganography is the most developed field, with several techniques are provided for different image formats. Therefore, the general image steganography including the fundamental concepts, the terminology, and the applications are highlighted in this paper. Further, the paper depicts the essential characteristics between information hiding and cryptography systems. In addition, recent well-known techniques in the spatial-domain steganography, such as LSB and pixel value differencing, are discussed in detail… More >

  • Open Access


    A Two-Stage Highly Robust Text Steganalysis Model

    Enlu Li1, Zhangjie Fu1,2,3,*, Siyu Chen1, Junfu Chen1

    Journal of Cyber Security, Vol.2, No.4, pp. 183-190, 2020, DOI:10.32604/jcs.2020.015010

    Abstract With the development of natural language processing, deep learning, and other technologies, text steganography is rapidly developing. However, adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis. If terrorists use the text steganography method to spread terrorist messages, it will greatly disturb social stability. Steganalysis methods, especially those for resisting adversarial attacks, need to be further improved. In this paper, we propose a two-stage highly robust model for text steganalysis. The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels. In the first phase, every sentence is first transformed into… More >

  • Open Access


    An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

    Junxiang Wang1, *, Lin Huang1, Ying Zhang1, Yonghong Zhu1, Jiangqun Ni2, Yunqing Shi3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 325-344, 2020, DOI:10.32604/cmc.2020.09784

    Abstract To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively… More >

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