1,721,185 research outputs found
Beyond traditional steganography: enhancing security and performance with spread spectrum image steganography
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Integrating Non-Positional Numbering Systems into E-Commerce Platforms: A Novel Approach to Enhance System Fault Tolerance
No full textIn the dynamic landscape of electronic commerce, the robustness of platforms is a critical determinant of operational continuity and trustworthiness, necessitating innovative approaches to fault tolerance. This study pioneers an advanced strategy for enhancing fault tolerance in e-commerce systems, utilizing non-positional numbering systems (NPNS) inspired by the mathematical robustness of the Chinese Remainder Theorem (CRT). Traditional systems rely heavily on positional numbering, which, despite its ubiquity, harbors limitations in flexibility and resilience against computational errors and system faults. In contrast, NPNS, characterized by their independence, equitability, and residue independence, introduce a transformative potential for system architecture, significantly increasing resistance to disruptions and computational inaccuracies. Our discourse extends beyond theoretical implications, delving into practical applications within contemporary e-commerce platforms. We introduce and elaborate on new terminologies, concepts, and a sophisticated classification system for fault-tolerance mechanisms within the framework of NPNS. This nuanced approach not only consolidates understanding but also identifies underexplored pathways for resilience in digital commerce infrastructure. Furthermore, this research highlights the empirical significance of adopting NPNS, offering a methodologically sound and innovative avenue to safeguard against system vulnerabilities. By integrating NPNS, platforms can achieve enhanced levels of redundancy and fault tolerance, essential for maintaining operational integrity in the face of unforeseen system failures. This integration signals a paradigm shift, emphasizing proactive fault mitigation strategies over reactive measures. Conclusively, this study serves as a seminal reference point for subsequent scholarly endeavors, advocating for a shift towards NPNS in e-commerce platforms. The practical adaptations suggested herein are poised to redefine stakeholders’ approach to system reliability, instigating a new era of confidence in e-commerce engagements
Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings
Full text linkBiometric authentication systems play a crucial role in modern security systems. However, maintaining the balance of privacy and integrity of stored biometrics derivative data while achieving high recognition accuracy is often challenging. Addressing this issue, we introduce an innovative image transformation technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models, which allows the distorted photo version to be stored for further verification. While initially intended for biometrics systems, the proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. By experimenting with widely used datasets LFW and MNIST, we show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy. We compare our method with previously state-of-the-art approaches. We publically release the source code
AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection
Full text linkBiometric security is the cornerstone of modern identity verification and
authentication systems, where the integrity and reliability of biometric
samples is of paramount importance. This paper introduces AttackNet, a bespoke
Convolutional Neural Network architecture, meticulously designed to combat
spoofing threats in biometric systems. Rooted in deep learning methodologies,
this model offers a layered defense mechanism, seamlessly transitioning from
low-level feature extraction to high-level pattern discernment. Three
distinctive architectural phases form the crux of the model, each underpinned
by judiciously chosen activation functions, normalization techniques, and
dropout layers to ensure robustness and resilience against adversarial attacks.
Benchmarking our model across diverse datasets affirms its prowess, showcasing
superior performance metrics in comparison to contemporary models. Furthermore,
a detailed comparative analysis accentuates the model's efficacy, drawing
parallels with prevailing state-of-the-art methodologies. Through iterative
refinement and an informed architectural strategy, AttackNet underscores the
potential of deep learning in safeguarding the future of biometric security
Enhancing copy-move forgery detection through a novel CNN architecture and comprehensive dataset analysis
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