Enhancing Privacy and Accuracy in Outsourced SIFT: Efficient Image Feature Extraction

Enhancing Privacy and Accuracy in Outsourced SIFT: Efficient Image Feature Extraction

Registration ID: IJNRD_219936

Published ID: IJNRD2404751

DOI: Click Here to Get

Jai Suriya S , David Prakash M , Krishna Kumar G , Ramanan K

Convolutional Neural Networks (CNNs), Scale Invariant Feature Transformation (SIFT), Class Similarity Network (CSN)

The advent of cloud computing has spurred the demand for secure and efficient picture recovery methods. This paper proposes a novel technique that harnesses the power of cloud infrastructure while prioritizing data security and retrieval accuracy. Central to the approach is the utilization of the Scale Invariant Feature Transformation (SIFT) algorithm for robust feature extraction from images. These features, encapsulating distinctive local image structures, serve as the basis for subsequent retrieval operations. Upon extraction, the system computes the Manhattan distances between the query images SIFT descriptors and those of images stored within the cloud database. This distance metric, known for its effectiveness in feature matching tasks, facilitates the identification of candidate images that closely resemble the query. To optimize retrieval efficiency, a balancing index tree structure is employed for organizing and storing the feature descriptors of images. This ensures rapid search operations, even in scenarios with a vast repository of images. Furthermore, stringent measures are implemented to safeguard data integrity and confidentiality. Before storage in the cloud, images and associated index are stored securely, mitigating risks associated with unauthorized access. The proposed technique offers a comprehensive solution for secure and efficient picture recovery in cloud environments. By integrating advanced image processing algorithms, efficient data structures, and robust encryption mechanisms, the system provides users with a reliable means to retrieve relevant images while safeguarding sensitive information.

Published Paper   E-Certificate

"Enhancing Privacy and Accuracy in Outsourced SIFT: Efficient Image Feature Extraction", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h431-h437, April-2024, Available :https://ijnrd.org/papers/IJNRD2404751.pdf

Volume 9 Issue 4, April-2024

Pages : h431-h437

Paper Reg. ID: IJNRD_219936

Published Paper Id: IJNRD2404751

Downloads: 000121122

Research Area: Engineering

Country: Salem, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404751.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404751

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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