11,983 research outputs found

    Reduced complexity enhancement of steganalysis of LSB-matching image steganography

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    We propose a method for steganalysis of still, grayscale images using a novel set of features that are extracted from images. This feature set employs the Gabor filter coefficients to train a multi-layer perceptron neural network and a support vector machine classifier. We show that incorporation of the Gabor filter coefficients to the feature sets of images could have a significant role in discrimination between clean and altered images. Experimental results show that the proposed method outperforms previous methods, introduced for steganalysis of LSB-matching image steganography, in terms of both discrimination accuracy and feature set dimensionality.</p

    High performance image steganography integrating IWT and Hamming code within secret sharing

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    Abstract Steganography and secret sharing are schemes to increase the security of private information against attackers. Steganography emphasizes secrecy while secret sharing distributes the secret key in shares that are conditionally classified to reconstruct the original secret. This paper introduces a counting‐based secret sharing scheme that aims to reduce the computational complexity for longer keys, thereby providing a practical steganographic approach for efficient sharing. The scheme integrates counting‐based secret sharing with integer wavelet transform (IWT) and steganography. The subscriptions created using the Hamming code are embedded in the cover image. Using this method, IWT significantly reduces the occurrence of common rounding errors. As a result, secret key extraction becomes very accurate and eliminates the need to access the original images. This design, using secret sharing based on counting, not only has simplicity and efficiency, but also features such as flexibility, scalability, and lack of central authority. In addition, high‐quality steganography was obtained for keys with lengths of 64, 256, 512, 1024, and 3072, showing an average PSNR=80.11 in different color images. This outstanding performance makes it a highly efficient alternative to previous designs, representing a groundbreaking contribution with significant public interest in the field

    Design and Build an Assessment Platform by Inserting Moodle-Based Cryptographic Methods

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    The use of digital platforms in the learning process is increasing, especially in the context of assessment activities. In this context, it is essential to realize that digital platforms can be subject to attack or fraud by irresponsible parties. It is due to the presence of sensitive data and/or restricted to a limited number of authorized persons. Therefore, the protection of data and its security in using digital platforms is very important. To enhance this layer of security in data protection, cryptographic methods play a crucial role in maintaining information security. By applying cryptographic methods to learning platforms for assessment purposes, we can increase the security and integrity of the data involved in the assessment process. This research aims to produce a plugin that can be used on a Moodle-based Learning Management System (LMS). This plugin will provide an additional activity in the form of an assessment activity with an essay exam type. When this plugin is used, all questions and answers will be encrypted into text that is difficult to understand by unauthorized parties when an attack attempt occurs. In this way, the learning platform for assessment purposes can safeguard and protect data from access by irresponsible parties

    Unleashing AI in Ethical Hacking: A Preliminary Experimental Study

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    This technical report details an experimental study aimed at evaluating the integration of AI, specifically ChatGPT, into ethical hacking. Conducted in a controlled virtual environment using a MacBook Pro host with VirtualBox 7, the study focused on assessing ChatGPT’s efficacy in aiding the penetration testing of target virtual machines, including one running Windows. This experiment was carried out to validate the claims made in the companion position paper, "Unleashing AI in Ethical Hacking". The primary aim was to explore ChatGPT’sutility in enhancing various stages of ethical hacking, such as Reconnaissance,Scanning, Gaining Access, Maintaining Access, and Covering Tracks. This technical report comprehensively documents the laboratory experiment and will be used to support the position paper, which is being prepared for conference presentation. The results underscore ChatGPT’s highly effective and remarkably helpful role in supporting and streamlining the penetration testing process

    Steganalysis of LSB based image steganography using spatial and frequency domain features

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    In this paper, we propose a method for steganalysis of grayscale images using both spatial and Gabor features. The basis of our work is to use Gabor filter coefficients and statistics of the graylevel co-occurrence matrix of images to train a support vector machine. We show that this feature set works well in steganalysis of grayscale images steganographied by LSB matching and S-tools.</p

    Predictive maintenance of rotational machinery using deep learning

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    This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability

    Hierarchal attribute based cryptographic model to handle security services in cloud environment: a new model

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    The sharing of information in the cloud is a unique element of the environment, but there is a risk that the information may land with the wrong people. To counterattack this problem, security-associated methodologies were used to secure the information that was readily available to clients. Despite the lack of benefits, this provides productive/adaptability and dependability in access control strategies between clients in the sharing of information. The novel hierarchal attribute-based cryptographic security model (NHACSM) is being proposed to provide adaptability, versatility, and access control in sharing information in the appropriate climate. This model allows clients to share information in a hierarchal way, allowing for a productive assessment of access control strategy and improved security. The NHACSM method is used to reduce the total time values for different user instances compared to conventional approaches, for example, attribute-set-based encryption (ASBE), key-policy attribute-based encryption (KP-ABE), and ciphertext-policy attribute-based encryption (CP-ABE). With respect to 10 instances existing methods achieve 2.7, 2.5, and 2.3 respectively, and also compared to 20, 30, 40, and 50 instances, our proposed method is low. The encryption and decryption time evaluation values and performance evaluation of different approaches, ASBE, CP-ABE, were taken into account when increasing the user instance

    DIP Paper Project

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    Image steganography, Information Hiding using AES algorithm</p

    Hybrid chaotic map with L-shaped fractal Tromino for image encryption and decryption

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    Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB

    Construction of Big Data Analysis Platform for College Students’ Sports Training Driven by Wireless Communication Network

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    College students’ physical training is an important aspect of their studies and lives. In light of the existing issues with the accuracy of the physical training evaluation index for college students and other issues. Based on the theory of wireless communication networks, model definition and stability analysis methods are used to optimize the original model. The optimization model of a wireless communication network is obtained by considering the data output control theory, trained by college students. This model can analyze the big data of college students’ physical training and construct the corresponding data platform. Through calculations, the rules for data changes under different indicators can be obtained. Finally, the accuracy of the optimized model is verified by comparing it with the original model. Related studies show that the variable parameters in the control system include model parameters, network upper limit, and corresponding control system parameters. On the whole, the model parameters exhibit a consistent trend of change, while the upper limit of the network displays a characteristic three-stage linear pattern. The control system first increases rapidly and then exhibits a gradual decline in exponential function type. The control system and the switching system can be switched using model calculations. The two different change curves exhibit obvious symmetry characteristics when influenced by the output times. It shows that the two change systems have opposite effects on the model, and the overall volatility is relatively high. The wireless communication network model can analyze and build a platform for collecting college students’ sports training data. According to the model calculation, it can be seen that the changes in different indexes exhibit clear linear characteristics. Among them, the concepts of sports, personalized training, and basic training show linearly increasing changes. However, the training structure and content exhibit a typical linear decline. Finally, the advantages of the wireless communication network model are illustrated with the experimental data. This study can provide guidance and ideas for the construction of a data platform for college students’ physical training
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