Secure IoT Systems Monitor Framework using Probabilistic Image Encryption

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-6, Issue-6, Jun-2020]
https://dx.doi.org/10.22161/ijaems.66.6 ISSN: 2454-1311
www.ijaems.com Page | 261
Secure IoT Systems Monitor Framework using
Probabilistic Image Encryption
P.G. Narmadha, K. Pooja, Mrs. K .Muthulakshmi
Department of IT, Panimalar Engineering College, Chennai, India
Abstract— In recent years, the modeling of human behaviors and patterns of activity for recognition or
detection of special events has attracted considerable research interest. Various methods abounding to build
intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the
observed dynamics of moving targets. Many systems include detection, storage of video information, and
human-computer interfaces. Here we present not only an update that expands previous similar surveys but also
a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications.
The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in
a manner that brings to attention key challenges.
Keywords— recognition, surveillance, abnormal human behavior.
I. INTRODUCTION1
Wireless Multimedia Surveillance Networks (WMSNs)
are part of this IoT-assisted environment , which consists of
visual sensors that observe the surrounding environment
from multiple overlapping views by continuously capturing
images, producing a large amount of visual data with
significant redundancy[1-3]. In the surveillance networks
research community it is generally understood that the visual
data obtained should be processed and that only the useful
data should be preserved for future use, such as irregular
event identification, case management , data interpretation
and video abstraction. The explanation for this is that, due to
resources and bandwidth limitations, transmitting all image
data across the transmission lines without processing is
inefficient. Additionally, the efficient extraction of
actionable intelligence from the sheer volume of surveillance
data[4] is comparatively difficult and time-consuming for an
analyst. Therefore, a mechanism that can collect semantically
important visual data autonomously must be exploited by
using the processing and transmission capabilities of modern
smart visual sensors Such a mechanism can allow the correct
view to be intelligently selected from multi-view surveillance
data captured from multiple sensors linked through IoT
infrastructure. It will allow real-time retrieval of the collected
1
.
data such that only valid data can be sent to the central
database for potential use. Currently there are networks of
video cameras available. The amount of data generated by
these vision sensor network installed in many settings
ranging from protection needs to environmental surveillance
easily satisfies big data requirements[5],[6]. The difficulties
in analyzing and processing such large video data are
apparent whenever an incident occurs which requires
foraging through vast video archives to identify interesting
events. As a consequence, video summarization, which has
gained considerable interest in recent years to automatically
retrieve a short and insightful description of these images.
Although video summarization has been extensively studied
in recent years, many previous methods focused primarily on
developing a variety of ways to summarize single-view
videos in the form of a key frame sequence or a video
skim[7-11]. Another big concern, though, although seldom
discussed in this sense, is providing an concise overview
from multi-view videos[14],[15]. Multi-view video
summarization refers to the question of summarization that
attempts to take a series of input images taken from various
cameras based on approximately the same fields-of-view
(fov) from different perspectives and to generate a video
synopsis or main frame sequence that depicts the most
important portions of the inputs within a short period (see
Fig . 1). In this article, given a range of videos and their

shots, we concentrate on creating an unsupervised approach
to choosing a subset of shots that make up the overview of
multiple views.Such a summary can be very beneficial in
many surveillance systems equipped in offices, banks,
factories, and crossroads of cities, for obtaining significant
information in short time.
Fig.1: cameras focusing on roughly the same fields-of-view
II. LITERATURE SURVEY
Rameswar Panda et al, in two important ways, represents
Multi-view video summary is different from single video
summary. First, although the quantity of multi-view data is
enormously challenging, a certain structure underlies it.
Specifically due to the locations and fields of view of the
cameras, there is a large amount of correlations in the data.
So to get an informative summary, content correlations as
well as discrepancies between different videos need to be
properly modeled. Secondly, for the same scenery, these
videos are captured with different view angles, and field
depth, resulting in a number of unaligned videos. Therefore,
variations in lighting, posture, point of view and
synchronization problems face a significant challenge in
summing up these images. Methods that attempt to derive
description from single-view videos do not produce an
appropriate set of members when summing up multi-view
videos.
A. A. Steffi et al. proposed a modified algorithm for
encryption and decryption of images using Lorenz and Baker
map. Among that, the authors presented encryption process
comprising of two stages: confusion and diffusion. In both
stages, the pixel positions and values are changed based on
one of two chaotic systems (Lorenz and Baker). To improve
security of the algorithm, separate keys are used for
generating the chaotic sequence. However, for decryption
stage, reverse operation is performed to obtained original
image.
X. Zhang et al. proposed an chaos-based image encryption
scheme based on large permutation with chaotic sequence
[12]. The image encryption scheme proposed in this paper
consists of multiple rounds of permutation and diffusion. The
permutation process is used to permute all the pixels. After
that, the diffusion process modified the pixel value. The
pseudo number is generated by logistic map. For the
decryption algorithm the only difference comes out in the
inverse of iteration. Their test results and analysis have
demonstrate that this scheme is much faster than the other
works suggested by Fridrich et al. [16], G. Chen et al. [17]
and G. Zhang et al. [18], because the proposed chaotic image
encryption is well suitable for real-time transmission.
III. PROPOSED SYSTEM
The main purpose of this survey is to identify existing
methods extensively, and to characterize the literature in a
manner that brings attention to key challenges. The block
diagram shown in Fig, for this proposed procedure. 2.
Fig.2: Proposed system block diagram
A.INPUT VIDEO
For multi-view surveillance videos recorded in industrial
environments, their processing capacities may be used to
evaluate the video stream to identify keyframes and then
delete obsolete and redundant visual data, thereby reducing
the requirements for bandwidth. In addition, keyframe
protection can be assured by applying the Gaussian blurring
theory, taking into account the computing capacities,
memory and transmission constraints.
B.FRAME CONVERSION
With each frame taken by the visual camera, the integral
image is computed, then background bootstrapping is done
which is important with eliminating background motion and

correct measurement of salient motion. Salt motion can be
measured in neighboring frames by calculating changes in
image block values. Even small background motion is
robust, as it uses temporal gradients based on the background
model and the integral image for salient motion. This can be
ascertained from Fig. 3, where the salient motion recognition
is demonstrated by our scheme using a few frames from a
reference film of an illegal border crossing.
Fig.3: Salient motion detection
C. PLANE SEPARATION
In the given sample video, there is significant motion clutter
due to the true and prediction that continuously change the
background pattern, like normal and blur state, thus making
the salient motion detection more challenging. Despite these
challenges, this approach detects the salient motion correctly,
as shown in Fig. 4.
Fig.4: Plane separation of salient motion detection
D.FEATURE EXTRACTION
Feature Extraction expects to decrease the number of
highlights in a dataset by making new features from the
current ones. This new diminished arrangement of the feature
should then have the option to outline the vast majority of the
data contained in the first arrangement of the feature. Right
now, outlined adaptation of the first features can be made
from a blend of the first set.
IV. DESIGN METHODOLOGY
A.CONVOLUTIONALNEURAL NETWORKS
A CNN contains is a supervised learning algorithm, for
training Multi-Layer Perceptions. It is a general, hierarchical
feature extractor that will map input image pixel intensities
into a feature vector. This will be classified by several fully
connected layers in the next step. All adjustable parameters
are optimized by minimizing the misclassification by
reducing the error over the training set. Each convolutional
layer performs a 2D convolution of its input maps with a
filter of different size 3 x 3, 5 x 5, 7 x 7. The subsequent
activations of the output maps are given by the total of the
past convolutional responses which are gone through a
nonlinear activation function. Max pooling layer will
perform the dimensionality reduction. The output of a thin
layer is given by the most extreme activation over non-
covering rectangular areas. Max-pooling makes location
invariance and down-samples the image along every
direction over the bigger neighborhood. Filter size of
convolutional and max-pooling layers are selected in such a
way that a fully connected layer can combine the output into
a one-dimensional vector. The last layer will always be a
fully connected layer which contains one output unit for all
classes. Here rectification linear unit is used as the activation
function.
B.IMAGE CLASSIFICATION BASED ON CNN
Fig.5.1: Image classification Using CNN

Object detection is the way toward discovering occasions
of certifiable items, for example, faces, structures, and bike
in pictures or recordings. Along these lines, the working
procedure of picture order dependent on the CNN appears on
Figure 4.1. Object detection calculations commonly use
separated highlights and learning calculations to perceive the
occurrences of an item class. It is regularly utilized in
applications, for example, picture recovery, security and
propelled driver help frameworks.
Fig.5.2 Flow Chart of of Detection
C. SOFTWARE SYSTEM
A. Python 2.5 / 3.5
Python is a scripting language of high quality, interpreted,
dynamic and object-oriented. Python is designed to be easily
readable Python is object-oriented − Python supports object-
oriented programming style or method encapsulating code
inside objects. Meanwhile, it released Python 3.0 in 2008.
Python 3 does not fit backwards with Python 2. Due to its
growing popularity as a scientific programming language and
the free availability of many state-of-the-art image
processing tools within its ecosystem, Python is an excellent
choice for these types of image processing tasks.
B.MACHINELEARNINGLIBRARIES AND
FRAMEWORKS
Many popular ML frameworks and libraries already offer the
possibility to use GPU accelerators to speed up learning
process with supported interfaces Some of them also allow
the use optimised libraries such as CUDA (cuDNN), and
OpenCL to improve the performance even further. The main
feature of many-core accelerators is that their massively
parallel architecture allows them to speedup computations
that involve matrix-based operations. The software
development in the ML/DL direction community is highly
dynamic and has various layers of abstraction as depicted.
A. OpenCV-Python
OpenCV ( Open Source Computer Vision Library) is one of
the most widely used computer-vision libraries. OpenCV-
Python forms the OpenCV Python API. Not only is
OpenCV-Python fast, since the background consists of code
written in C / C++, it is also easy to code and deploy
(because of the Python wrapper in the foreground). This
makes computationally intensive computer vision programs a
great choice.
4.5.4 DEEP LEARNING LIBRARIESAND
FRAMEWORKS
Deep learning ( DL) is an artificial intelligence branch that
allows computers the ability to learn without express
programming. Here machine is trained to identify objects of
various kinds. Object image is given as an input to the
machine, and the processor tells if it is the same object or
not. Until the DL era, the apps were selected and designed
manually, and then a classifier followed. The revolutionary
part of ML is that features are mostly learned automatically
by using Convolutionary Neural Network (CNN) from the
training data. CNN's use renders a classifier efficient in
image recognition process. Deep learning is a branch of
machine learning that has some of the best results in these
fields. In order to facilitate the implementation of those
approaches, a set of software frameworks have been
developed and are currently available. TensorFlow is the
second generation of Google artificial intelligence learning
system library that has got much attention and affirmation in
the field of the machine learning in all over the world.
TensorFlow is written with a Python API over a C/C++
engine, this makes it run faster. It is available on Linux, Mac,
Windows OS and embedded platforms like Android OS and
Raspberry Pi. It provides good accuracy with better detection

speed. Its detection speed can improve by efficient
algorithm.
A. Tensorflow
TensorFlow is an open-source numerical computing
software library using data flow graphs TensorFlow was
created and maintained by the Google Brain team within the
Machine Intelligence research organization for ML and DL
at Google. This is officially available under the open-source
license Apache 2.0. TensorFlow is designed for the
distributed testing and inference on a wide scale. Nodes in
the graph represent mathematical operations, while the edges
of the graph represent the shared multidimensional data
arrays (tensors) between them. The distributed architecture
of TensorFlow involves centralized master and worker
facilities of Kernel implementations. These include 200
standard operations written in C++ including mathematical
operations, array manipulation, control flow, and state
management operations. TensorFlow is designed for use in
research, production and manufacturing systems. It can run
hundreds of nodes on single CPU systems, GPUs, handheld
devices, and distributed large scale networks.
V. RESULTS AND DICUSSION
In this, the project presents experimental results and
discuss the suitability of the best performing representation
and model over the others. The architecture our model is
based on classification of CNN with normal and abnormal
activities using CCTV surveillance. In the figure 5.1
represents the feature extraction of the sample input images.
Fig.5.3: Results of Normal Stages Classification
The training validation uses for conduct experiments to have
fair validation of the performance of the proposed approach. In
Figure 5.3 presents the results of the experiments for the
classification of ‘Normal’ vs. ‘Abnormal Images’ using
proposed CNN and the existing method used for comparison
for both the datasets.
Fig.5.3: Results of Abnormal Stages Classification
VI. CONCLUSION
A significant amount of redundant video data is generated
thanks to recent advances in IoT-assisted surveillance
networks in industrial environments. Its transmission,
analysis and management are difficult and demanding,
requiring prioritization of the image. For this job, an
effective video description approach is first used to retrieve
the informative frames from the video surveillance data and
can be used to identify suspicious activities. As the derived
keyframes are essential for further research, their privacy and
protection during transmission is of utmost importance.
Hence, we proposed a quick probabilistic keyframe prior to
transmission, taking into account the memory and processor
requirements of restricted devices, which enhance its
suitability for industrial IoT systems.
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