Integrasi Discrete Wavelet Transform dan Singular Value Decomposition pada Watermarking Citra untuk Perlindungan Hak Cipta

Tren masalah watermarking pada sekarang ini adalah bagaimana mengoptimalkan trade-off antara imperceptibility (visibilitas) citra ter-watermark terhadap pengaruh distorsi dan robustness terhadap penyisipan watermark. Masalah menggunakan kekuatan penyisipan berdasarkan Single Scaling Factor (SSF) atau Multiple Scaling Factor (MSF) juga ditemukan. Penelitian ini mengusulkan metode penyisipan watermark untuk perlindungan hak cipta pada citra dan algoritma ekstraksi citra ter-watermark yang dioptimalkan dengan penggabungan Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD). Nilai-nilai singular dari LL3 koefisien sub-band dari citra host dimodifikasi menggunakan nilai tunggal citra watermark biner menggunakan MSFs. Kontribusi utama dari skema yang diusulkan adalah aplikasi DWT-SVD untuk mengidentifikasi beberapa faktor skala yang optimal. Hasil penelitian menunjukkan bahwa skema yang diusulkan menghasilkan nilai Peak Signal to Noise Ratio (PSNR) yang tinggi, yang menunjukkan bahwa kualitas visual gambar yang baik pada masalah citra watermarking telah mengoptimalkan trade-off. Trade-off antara imperceptibility (visibilitas) citra ter-watermark terhadap pengaruh distorsi dan robustness citra ter-watermark terhadap operasi pengolahan citra. Nilai PSNR yang didapat pada citra yang diujikan: baboon=53,184; boat=53,328; cameraman=53,700; lena=53,668; man=53,328; dan pepper sebesar 52,662. Delapan perlakuan khusus pada hasil citra ter-watermark diujikan dan diekstraksi kembali yaitu JPEG 5%, Noise 5%, Gaussian filter 3x3, Sharpening, Histogram Equalization, Scaling 512-256, Gray Quantitation 1bit, dan Cropping 1/8. Hasil dari perlakuan khusus kemudian diukur nilai Normalized Cross-Correlation (NC) yang menghasilkan rata-rata semua citra diperoleh sebesar 0,999 dari satu. Hasil penelitian dari metode yang diusulkan lebih unggul nilai PSNR dan NC dari penelitian sebelumnya. Jadi dapat disimpulkan bahwa penerapan dengan metode DWT-SVD ini mampu menghasilkan citra yang robust namun memiliki tingkat imperceptibility yang cukup tinggi

Secured Mechanism Towards Integrity of Digital Images Using DWT, DCT, LSB and Watermarking Integrations

"Watermarking" is one method in which digital information is buried in a carrier signal; the hidden information should be related to the carrier signal. There are many different types of digital watermarking, including traditional watermarking that uses visible media (such as snaps, images, or video), and a signal may be carrying many watermarks. Any signal that can tolerate noise, such as audio, video, or picture data, can have a digital watermark implanted in it. A digital watermark must be able to withstand changes that can be made to the carrier signal in order to protect copyright information in media files. The goal of digital watermarking is to ensure the integrity of data, whereas steganography focuses on making information undetectable to humans. Watermarking doesn't alter the original digital image, unlike public-key encryption, but rather creates a new one with embedded secured aspects for integrity. There are no residual effects of encryption on decrypted documents. This work focuses on strong digital image watermarking algorithms for copyright protection purposes. Watermarks of various sorts and uses were discussed, as well as a review of current watermarking techniques and assaults. The project shows how to watermark an image in the frequency domain using DCT and DWT, as well as in the spatial domain using the LSB approach. When it comes to noise and compression, frequency-domain approaches are far more resilient than LSB. All of these scenarios necessitate the use of the original picture to remove the watermark. Out of the three, the DWT approach has provided the best results. We can improve the resilience of our watermark while having little to no extra influence on image quality by embedding watermarks in these places.

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words