This paper investigates two different blind watermarking systems in the frequency domain with the development of a Pseudo Random Number Generator (PRNG), based on a fractional-order chaotic system, for watermark encryption. The methodology is based on converting the cover image to the YCbCr color domain and applying two different techniques of frequency transforms, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), to the Y channel. Then, the encrypted watermark is embedded in the middle-frequency band and HH band coefficients for the DCT and DWT, respectively. For more security and long encryption key size, the fractional-order Lorenz system is used to double the encryption key size and make it secure against brute-force attacks. The proposed algorithms successfully detect the hidden watermark by using the statistical properties of the embedding media, where the PRNG is examined using statistical tests and the watermarking systems are evaluated using standard imperceptibility and robustness measures. Common attacks such as noise-adding attacks, image enhancement attacks and geometric transformation attacks are discussed. Results of the PRNG demonstrate sensitivity to the system parameters, and results of the watermarking systems show good imperceptibility while keeping the robustness measures in a good range. © 2023 Elsevier GmbH
Analysis and Guidelines for Different Designs of Pseudo Random Number Generators
The design of an efficient Pseudo Random Number Generator (PRNG) with good randomness properties is an important research topic because it is a core component in many applications. Based on an extensive study of most PRNGs in the past few decades, this paper categorizes six distinct design scenarios under two primary groups: non-chaotic and chaotic generators. The non-chaotic group comprises Linear Feedback Shift Registers (LFSR) with S-Boxes, primitive roots, and elliptic curves, whereas the chaotic group encompasses discrete, continuous, and fractional-order chaotic generators. This paper delves into the related scientific summaries, equations, flowcharts, and designs with necessary recommendations for each PRNG scenario. Even though the focus is on the basic design characteristics that provide simple, functional and secure PRNGs, it is possible to enhance those designs for additional features and improved efficiency. Simulation outcomes and system key configurations, which produce long random sequences, are also presented and evaluated using leading criteria. The evaluation criteria include the National Institute of Standards and Technology (NIST) SP-800-22 test suite, TestU01 randomness tests, histogram, entropy, autocorrelation, and cross-correlation. Furthermore, key space, key sensitivity, and bit rate indicate that all designed examples meet international standards with high quality. The presented PRNGs are compared and integrated into an image encryption system. Although each PRNG design scenario can have a different key space, simple designs with fixed-length system keys are chosen for the sake of proper comparisons. Statistical and security assessments of the encryption system demonstrate that the PRNGs are cryptographically secure. © 2013 IEEE.
Chaotic neural network quantization and its robustness against adversarial attacks
Achieving robustness against adversarial attacks while maintaining high accuracy remains a critical challenge in neural networks. Parameter quantization is one of the main approaches used to compress deep neural networks to have less inference time and less storage memory size. However, quantization causes severe degradation in accuracy and consequently in model robustness. This work investigates the efficacy of stochastic quantization to enhance robustness and accuracy. Noise injection during quantization is explored to understand the impact of noise types and magnitudes on model performance. A comprehensive comparison between different applying scenarios for stochastic quantization and different noise types and magnitudes was implemented in this paper. Compared to the baseline deterministic quantization, chaotic quantization achieves a comparable accuracy, however, it achieves up to a 43% increase in accuracy against various attack scenarios. This highlights stochastic quantization as a promising defense mechanism. In addition, there is a crucial role played by the choice of noise type and magnitude in stochastic quantization. Lorenz and Henon noise distributions in stochastic quantization outperform traditional uniform and Gaussian noise in defending against attacks. A transferability analysis was discussed to understand the generalizability and effectiveness of the proposed stochastic quantization techniques. A cross-validation definition was newly evaluated in this scope to analyse the model’s stability and robustness against attacks. The study outperformed a quantization network technique and improved the model’s robustness and stability against adversarial attacks using chaotic quantization instead of deterministic quantization or even instead of stochastic quantization using traditional noise. © 2024 Elsevier B.V.
Optimization of Double fractional-order Image Enhancement System
Image enhancement is a vital process that serves as a tool for improving the quality of a lot of real-life applications. Fractional calculus can be utilized in enhancing images using fractional order kernels, adding more controllability to the system, due to the flexible choice of the fractional order parameter, which adds extra degrees of freedom. The proposed system merges two fractional order kernels which helps in image enhancement techniques, and the contribution of this work is based on the study of how to optimize this process. The optimization of the two fractional kernels was done using the neural network optimization algorithm (NNA) to utilize the best order for the two kernels. In this paper, three fractional kernels are studied to highlight the performance of image enhancement using fractional kernels against different metrics. Furthermore, three different combinations of two kernels are combined and studied to enhance the metrics score by utilizing two different fractional orders for each kernel. Various optimization algorithms are used to obtain the optimum fractional order for both single and combined kernels. Using the constrained NNA, the evaluation metrics of the image enhancement show a 33% increase in measure of enhancement metric (EME), 21% increase in contrast, and 4% increase in average gradient compared to the best-achieved metrics by the literature while keeping the similarity metric above 0.75. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.