Improved Color Image Watermarking Using Logistic Maps and Quaternion Legendre-Fourier Moments

Lashin

et al. 2021

Sensors

Self Cite

This paper proposes a new blind, color image watermarking method using fast Walsh–Hadamard transformation (FWHT) and multi-channel fractional Legendre–Fourier moments (MFrLFMs). The input host color image is first split into 4 × 4 non-interfering blocks, and the MFrLFMs are computed for each block, where proper MFrLFMs coefficients are selected and FWHT is applied on the selected coefficients. The scrambled binary watermark has been inserted in the quantized selected MFrLFMs coefficients. The proposed method is a blind extraction, as the original host image is not required to extract the watermark. The proposed method is evaluated over many visual imperceptibility terms such as peak signal-to-noise ratio (PSNR), normalized correlation (NC), and bit error rate. The robustness of the proposed method is tested over several geometrical attacks such as scaling, rotation, cropping, and translation with different parameter values. The most widely recognized image processing attacks are also considered, e.g., compressing and adding noise attacks. A set of combination attacks are also tested to analyze the robustness of the proposed scheme versus several attacks. The proposed model’s experimental and numerical results for invisibility and robustness were superior to the results of similar watermarking methods.

Section: Related Workmentioning

confidence: 99%

Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments

Abdel-Aziz

Lashin

et al. 2021

Sensors

Self Cite

“…The robust image watermarking algorithms has three stages: Embedding, Detection, and Extraction of the watermark [9]. We embed a secret invisible signature in the host image in the embedding process.…”

Section: Introductionmentioning

confidence: 99%

Robust color image watermarking using multi-core Raspberry pi cluster

Magdi

Lashin

et al. 2022

Multimed Tools Appl

Self Cite

Image authentication approaches have gotten a lot of interest recently as a way to safeguard transmitted images. Watermarking is one of the many ways used to protect transmitted images. Watermarking systems are pc-based that have limited portability that is difficult to use in harsh environments as military use. We employ embedded devices like Raspberry Pi to get around the PC’s mobility limitations. Digital image watermarking technology is used to secure and ensure digital images’ copyright by embedding hidden information that proves its copyright. In this article, the color images Parallel Robust watermarking algorithm using Quaternion Legendre-Fourier Moment (QLFM) in polar coordinates is implemented on Raspberry Pi (RPi) platform with parallel computing and C++ programming language. In the host image, a binary Arnold scrambled image is embedded. Watermarking algorithm is implemented and tested on Raspberry Pi model 4B. We can combine many Raspberry Pi’s into a ‘cluster’ (many computers working together as one) for high-performance computation. Message Passing Interface (MPI) and OpenMP for parallel programming to accelerate the execution time for the color image watermarking algorithm implemented on the Raspberry Pi cluster.

“…A novel color face recognition method called multi-channel orthogonal fractional-order moment proposed by Hosny, Abd Elaziz & Darwish (2021) , and this method proved its efficiency, invariance to transformation, and robustness to noise. Orthogonal moments proved its efficiency in various pattern recognition applications such as color image watermark ( Darwish, Hosny & Kamal, 2020 ), Ranade & Anand (2021) proposed color face recognition technique based on Zernike quaternion moment vector and using quaternion vector moment (QVM) similarity distance to enhance accuracy, and the experiment proved its superiority on all other techniques. Abdelmajid et al introduced a face recognition system based on quaternion moment and deep neural network (DNN), it is computationally low cost and also accurate ( Alami et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate face recognition system using MORSCMs-LBP on embedded circuits

Hamad

Elkomy

et al. 2022

PeerJ Computer Science

Self Cite

Because of the current COVID-19 circumstances in the world and the tremendous technological developments, it has become necessary to use this technology to combat the spread of the new coronavirus. The systems that depend on using hands, such as fingerprint systems and PINs in ATM systems, could lead to infection, so they have become undesirable and we can replace them by using facial recognition instead. With the development of technology and the availability of nano devices like the Raspberry Pi, such applications can be implemented easily. This study presents an efficient face recognition system in which the face image is taken by a standalone camera and then passed to the Raspberry Pi to extract the face features and then compare them with the database. This approach is named MORSCMs-LBP by combining two algorithms for feature extraction: Local Binary Pattern (LBP) as a local feature descriptor and radial substituted Chebyshev moments (MORSCMs) as a global feature descriptor. The significant advantage of this method is that it combines the local and global features into a single feature vector from the detected faces. The proposed approach MORSCMs-LBP has been implemented on the Raspberry Pi 4 computer model B with 1 GB of RAM using C++ OpenCV. We assessed our method on various benchmark datasets: face95 with an accuracy of 99.0278%, face96 with an accuracy of 99.4375%, and grimace with 100% accuracy. We evaluated the proposed MORSCMs-LBP technique against other recently published approaches; the comparison shows a significant improvement in favour of the proposed approach.