Authors :
Dr. G. Amudha; Aadhithan. P; Mano. S
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/yma6cb9w
Scribd :
http://tinyurl.com/fwpv7dtu
DOI :
https://doi.org/10.5281/zenodo.10650100
Abstract :
With the proliferation of digital images,
there has been considerable research into Contentbased Image Retrieval (CBIR) techniques. Typically,
CBIR services demand substantial computational and
storage resources, making it advantageous to
outsource these services to cloud servers equipped
with abundant resources. However, the challenge
arises in ensuring privacy, given the inherent lack of
complete trust in cloud servers. In here, we introduce a
delegated CBIR approach adapted from a book
BOEW model. Our method involves encrypting
images through hue value replacement, block and
intra-block pixel permutation. Subsequently, cloud
server calculates regional histograms from these
protected image blocks, binds and employs the
resulting cluster centers as encrypted viewable words.
It is this approach allows us to construct a Bagof-Encrypted-Words (BOEW) model, representing
each hue as a feature vector—specifically, a
generalized histogram of the encrypted viewable
words. To measure the resemblance between images,
we utilize the Manhattan space between feature
vectors on the cloud server. Experimental output and
a security analysis of our proffered scheme illustrate
its search precision and security.
With the proliferation of digital images,
there has been considerable research into Contentbased Image Retrieval (CBIR) techniques. Typically,
CBIR services demand substantial computational and
storage resources, making it advantageous to
outsource these services to cloud servers equipped
with abundant resources. However, the challenge
arises in ensuring privacy, given the inherent lack of
complete trust in cloud servers. In here, we introduce a
delegated CBIR approach adapted from a book
BOEW model. Our method involves encrypting
images through hue value replacement, block and
intra-block pixel permutation. Subsequently, cloud
server calculates regional histograms from these
protected image blocks, binds and employs the
resulting cluster centers as encrypted viewable words.
It is this approach allows us to construct a Bagof-Encrypted-Words (BOEW) model, representing
each hue as a feature vector—specifically, a
generalized histogram of the encrypted viewable
words. To measure the resemblance between images,
we utilize the Manhattan space between feature
vectors on the cloud server. Experimental output and
a security analysis of our proffered scheme illustrate
its search precision and security.
