Tamper localization and recovery watermarking scheme can be used to detect manipulation and recover tampered images. In this paper, a tamper localization and lossless recovery scheme that used region of interest (ROI) segmentation and multilevel authentication was proposed. The watermarked images had a high average peak signal-to-noise ratio of 48.7 dB and the results showed that tampering was successfully localized and tampered area was exactly recovered. The usage of ROI segmentation and multilevel authentication had significantly reduced the time taken by approximately 50 % for the tamper localization and recovery processing.
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http://dx.doi.org/10.1007/s10278-012-9484-4 | DOI Listing |
Sci Rep
January 2025
PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more.
View Article and Find Full Text PDFRev Sci Instrum
December 2024
Applied Physics Division, Soreq NRC, Yavne 81800, Israel.
We present a design and first use of a kJ level laser facility for research of non-local thermodynamic equilibrium atomic physics using the buried layer target method. The target design included a metal layer buried inside a plastic tamper with thicknesses tailored to the expected laser intensities. The target was illuminated from each side by two laser beams with intensities of 0.
View Article and Find Full Text PDFEnviron Monit Assess
November 2024
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, School of Water Resources and Hydropower, Xi'an University of Technology, Xi'an, 710048, China.
The efficiency of water distribution at primary, secondary, and tertiary levels in the Indus Basin Irrigation System (IBIS) has historically suffered due to poor design, suboptimal operation, and water scarcity. To address these issues, the system has been designed with ungated irrigation outlets to ensure equitable water allocation at secondary and tertiary levels. This research evaluates the hydraulic performance of three irrigation outlets: adjustable proportional module (APM), adjustable orifice semi-module (AOSM), and open flume (OF) using a physical model study.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure.
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