Image Security Based on Rotational Visual Cryptography

Karthik, Ragipati; Baji, Veluthurla Mahammad Mobin; Kumar, Mittapalli Pavan; Anjum, Shiza Arman; Merugu, Suresh

doi:10.1007/978-981-19-3045-4_10

Cited by 3 publications

(1 citation statement)

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“…This viewpoint opens an entirely new line of research where the utility of many powerful tools available in the field of image processing for SAE can be explored. Image processing is a very mature field of knowledge where a diverse range of sophisticated and advanced tools have been developed to detect a broad range of shapes and patterns in images typically obtained in many application scenarios [17][18][19][20][21][22][23][24][25][26]. Many existing techniques, despite generally have been proposed and developed in the different application scenarios, have the potential to effectively contribute to the problem of SAE considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Signal Area Estimation in Radio-Communication Spectrograms Based on Morphological Image Processing

Alammar,

López-Benítez,

Lehtomäki

et al. 2023

Wireless Communications and Mobile Computing

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The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

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