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  • Open Access


    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

    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 the initial coefficients of each… More >

  • Open Access


    Robustness Convergence for Iterative Learning Tracking Control Applied to Repetitfs Systems

    Ben Attia Selma*, Ouerfelli Houssem Eddine, Salhi Salah

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 795-810, 2022, DOI:10.32604/iasc.2022.020435

    Abstract This study addressed sufficient conditions for the robust monotonic convergence of repetitive discrete-time linear parameter varying systems, with the parameter variation rate bound. The learning law under consideration is an anticipatory iterative learning control. Of particular interest in this study is that the iterations can eliminate the influence of disturbances. Based on a simple quadratic performance function, a sufficient condition for the proposed learning algorithm is presented in terms of linear matrix inequality (LMI) by imposing a polytopic structure on the Lyapunov matrix. The set of LMIs to be determined considers the bounds on the rate of variation of the… More >

  • Open Access


    Enhancing the Robustness of Visual Object Tracking via Style Transfer

    Abdollah Amirkhani1,*, Amir Hossein Barshooi1, Amir Ebrahimi2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 981-997, 2022, DOI:10.32604/cmc.2022.019001

    Abstract The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy… More >

  • Open Access


    A Parametric Study of Arabic Text-Based CAPTCHA Difficulty for Humans

    Suliman A. Alsuhibany*, Hessah Abdulaziz Alhodathi

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 523-537, 2022, DOI:10.32604/iasc.2022.019913

    Abstract The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed scheme, we ran four filter… More >

  • Open Access


    Toward Robust Classifiers for PDF Malware Detection

    Marwan Albahar*, Mohammed Thanoon, Monaj Alzilai, Alaa Alrehily, Munirah Alfaar, Maimoona Algamdi, Norah Alassaf

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2181-2202, 2021, DOI:10.32604/cmc.2021.018260

    Abstract Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM)… More >

  • Open Access


    A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

    Fahd N. Al-Wesabi1,2,*, Saleh Alzahrani3, Fuad Alyarimi3, Mohammed Abdul3, Nadhem Nemri3, Mohammed M. Almazah4

    Computer Systems Science and Engineering, Vol.37, No.3, pp. 297-311, 2021, DOI:10.32604/csse.2021.015915

    Abstract In this paper, a combined approach CAZWNLP (a combined approach of zero-watermarking and natural language processing) has been developed for the tampering detection of English text exchanged through the Internet. The third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this study. The third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given… More >

  • Open Access


    Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

    Ganeshan Keerthana1,*, Panneerselvam Anandan2, Nandhagopal Nachimuthu3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 903-917, 2021, DOI:10.32604/cmc.2020.012255

    Abstract Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced… More >

  • Open Access


    Robust Design Optimization and Improvement by Metamodel

    Shufang Song*, Lu Wang, Yuhua Yan

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 383-399, 2020, DOI:10.32604/cmes.2020.09588

    Abstract The robust design optimization (RDO) is an effective method to improve product performance with uncertainty factors. The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables. There are some important issues in RDO, such as how to judge robustness, deal with multi-objective problem and black-box situation. In this paper, two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment. The robustness measure based on maximum entropy is proposed. Weighted sum method is improved to deal with the objective function, and the basic framework of… More >

  • Open Access


    Prediction of Vibration Response of Satellite Equipment Panel in Consideration of Robustness

    Y. Adachi1, F. Sako1, S. Qinzhong2, S. Ando2, M. Tsuchihashi2, I. Hagiwara3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.7, No.1, pp. 19-22, 2008, DOI:10.3970/icces.2008.007.019

    Abstract In this paper, the vibration response of the satellite equipment panel during the launch phase is predicted in consideration of the robustness. By using a simplified model consisted of a panel and an acoustic cavity, Monte Carlo simulation in which analytical parameters are changed at random in the conceivable range of variation is done. From the result, the acceleration power spectral density (PSD) of the satellite equipment panel is obtained. After statistical processing, the effect of variation on the vibration response is estimated and the proper design criteria of the equipment are defined. More >

  • Open Access


    A Novel Quantum Stegonagraphy Based on Brown States

    Zhiguo Qu1,*, Tiancheng Zhu2, Jinwei Wang1, Xiaojun Wang3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 47-59, 2018, DOI: 10.3970/cmc.2018.02215

    Abstract In this paper, a novel quantum steganography protocol based on Brown entangled states is proposed. The new protocol adopts the CNOT operation to achieve the transmission of secret information by the best use of the characteristics of entangled states. Comparing with the previous quantum steganography algorithms, the new protocol focuses on its anti-noise capability for the phase-flip noise, which proved its good security resisting on quantum noise. Furthermore, the covert communication of secret information in the quantum secure direct communication channel would not affect the normal information transmission process due to the new protocol’s good imperceptibility. If the number of… More >

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