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All Journal International Journal of Information Engineering and Science Ignite: Journal Islamic Global Network for Information Technology and Entrepreneurship
Natasha Fedora Barus
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Computer Science & IT Engineering

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Literature Review on Histogram-Based Image Forensics for Recaptured Image Detection Nathanael David Christian Barus; Nayem Kibriya; Natasha Fedora Barus
International Journal of Information Engineering and Science Vol. 1 No. 3 (2024): August : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i3.35

Abstract

This qualitative literature review explores the realm of histogram-based image forensics for recaptured image detection, addressing the challenges posed by advancements in display technology and the subsequent need for robust forensic techniques. The research methodology involves a systematic approach, including defined research objectives, thorough literature search, data extraction, thematic analysis, and ethical considerations. The focal point is the proposed method utilizing Local Ternary Count (LTC) histograms normalized from residue maps, demonstrating exceptional performance across various databases. The methodology involves residue map calculation, LTC histogram extraction, and experiments showcasing the method's efficiency in both single and mixed databases. The discussion emphasizes emerging frontiers in recaptured image forensics, presenting innovative algorithms categorized by the medium used during the recapture process. The shift towards deep learning methods is noted, with a focus on a proposed algorithm for detecting images recaptured from LCD screens based on quality-aware features and histogram features. The RID field has witnessed advancements, with a detailed overview of methods categorically addressing recapture from LCD screens. Ethical considerations are integrated into the discussion, and the conclusion emphasizes the need for constant adaptation, innovation, and collaboration in the fight against evolving manipulation techniques. Looking ahead, the fusion of features, standardized datasets, and advanced deep learning architectures are identified as key elements for future research in ensuring image authenticity
Development Of Data Security Algorithms: A Literature Review On Information Security In The Context Of Big Data Nathanael David Christian Barus; Natasha Fedora Barus
Journal Islamic Global Network for Information Technology and Entrepreneurship Vol. 2 No. 1 (2024): January : Journal Islamic Global Network for Information Technology and Entrepr
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/ignite.v2i1.933

Abstract

In the era of Big Data, securing sensitive information and ensuring data integrity have become paramount concerns due to the unprecedented volume and intricacy of data. Traditional security algorithms face significant challenges in adapting to the distinct characteristics of Big Data. This literature review explores the evolution of data security algorithms tailored explicitly for the Big Data landscape, aiming to address the increasing demand for robust security solutions capable of handling the unique challenges posed by the massive scale and complexity of data. By scrutinizing existing literature, the review unveils advancements, trends, and innovations developed by researchers and practitioners to mitigate vulnerabilities associated with handling vast datasets. The review also sheds light on emerging technologies and cryptographic techniques specifically designed for Big Data security, contributing to enhanced confidentiality, integrity, and availability in the face of evolving cyber threats. While these developments offer advantages such as improved data protection and threat detection, the review highlights challenges, including algorithmic bias, computational complexity, privacy trade-offs, and a shortage of skilled workforce. By considering these factors and emphasizing continuous improvement and ethical considerations, organizations can responsibly leverage data security algorithms to enhance information security in the era of Big Data.
Co-Authors Nathanael David Christian Barus Nayem Kibriya