Authors :
Mahesh Enumula; Dr. M. Giri; Dr. V. K. Sharma
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/muwdjwfz
DOI :
https://doi.org/10.5281/zenodo.8289379
Abstract :
Cybersecurity has become a serious threat to
society because of the revolution on the internet. Due to
the internet revolution worldwide people are consuming
quintillion bytes of data on daily basis. The data
consumption over the internet may increase in the feature
at the same time the threats to internet security posing
new questions to the world. One of the major problems in
cybersecurity is image forgery. An effective mechanism to
detect image forgery is needed to avoid complications in
various fields like medical imaging, space research,
defense, etc., where even small details in the images are
very crucial. In the present research by taking the
advantage of Artificial intelligence an effective model is
built. This model in the pre-processing stage of the image
uses superpixels. These features will be provided as inputs
to the deep neural network. Basically, the neural network
acts as a classifier of the images. The convolutional neural
networks are built and optimized according to the input
data. The convolutional neural networks are being
trained by a large number of image data set and will be
tested for the results. When the trained CNN is supplied
with the images which are needed to be detected for the
forgery in the initial stages the images will be divided into
blocks that are non-uniform and features will be
extracted which consists of superpixels. These features
will be supplied to the classifier. The classifier not only
detects the forged image and non forged image but also
indicates the location of the forgery.
The present research paper compares various
methods of image forgery detection. In the comparison,
the proposed method will enhance performance matrices
in terms of accuracy, precession, Recall, etc.
Keywords :
Forgery detection, Deep neural network, Artificial intelligence, Convolutional neural network, superpixels, Feature extraction, accuracy, precession, recall, confusion matrix.
Cybersecurity has become a serious threat to
society because of the revolution on the internet. Due to
the internet revolution worldwide people are consuming
quintillion bytes of data on daily basis. The data
consumption over the internet may increase in the feature
at the same time the threats to internet security posing
new questions to the world. One of the major problems in
cybersecurity is image forgery. An effective mechanism to
detect image forgery is needed to avoid complications in
various fields like medical imaging, space research,
defense, etc., where even small details in the images are
very crucial. In the present research by taking the
advantage of Artificial intelligence an effective model is
built. This model in the pre-processing stage of the image
uses superpixels. These features will be provided as inputs
to the deep neural network. Basically, the neural network
acts as a classifier of the images. The convolutional neural
networks are built and optimized according to the input
data. The convolutional neural networks are being
trained by a large number of image data set and will be
tested for the results. When the trained CNN is supplied
with the images which are needed to be detected for the
forgery in the initial stages the images will be divided into
blocks that are non-uniform and features will be
extracted which consists of superpixels. These features
will be supplied to the classifier. The classifier not only
detects the forged image and non forged image but also
indicates the location of the forgery.
The present research paper compares various
methods of image forgery detection. In the comparison,
the proposed method will enhance performance matrices
in terms of accuracy, precession, Recall, etc.
Keywords :
Forgery detection, Deep neural network, Artificial intelligence, Convolutional neural network, superpixels, Feature extraction, accuracy, precession, recall, confusion matrix.
