PICSEC: PICTURE SECURITY

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IRJCS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.81 –SJIF: Innospace, Morocco (2016): 4.281 Indexcopernicus: (ICV 2016): 88.80

PICSEC: PICTURE SECURITY

Sadia Marium¹,Sahana Varadharajan², Somesh D Savanth³, Suchitra Pandey⁴, Mr. B.V Vinay⁵

1,2,3,4 UG student, Department of ISE, Vemana Institute of Technology, Bangalore,

5Assistant Professor, Department of ISE, Vemana Institute of Technology, Bangalore.

1[email protected], [email protected]

[email protected],³[email protected],⁴[email protected], ⁵ Manuscript History

Number: IRJCS/RS/Vol.06/Issue06/JNCS10128 Received: 29, May 2019

Final Correction: 30, May 2019 Final Accepted: 02, June 2019 Published: June 2019

doi://10.26562/IRJCS.2019.JNCS10128

Editor: Dr.A.Arul L.S, Chief Editor, IRJCS, AM Publications, India

Copyright:©2019 This is an open access article distributed under the terms of the Creative Commons Attribution License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

ABSTRACT -Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs).

Unfortunately, it may leak users’ privacy if they are allowed to post, comment, and tag a photo freely. In project, we attempt to address this issue and when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users’ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy.

I. INTRODUCTION

Online Social Networks (OSNs) have become integral part of our daily life and has profoundly changed the way we interact with each other, fulfilling our social needs–the needs for social interactions, information sharing, appreciation and respect. It is also the nature of social media that makes people put more content, including photos, over OSNs without too much thought on the content. However, once something, such as a photo, is posted online, it becomes a permanent record, which may be used for purposes we never expect. For example, a posted photo in a party may reveal a connection of a celebrity to a mafia world. Because OSN users may be careless in posting content while the effect is so far-reaching, privacy protection over OSNs becomes an important issue.

Comparing with previous works, our contributions are as follows:

1. Here the potential owners of shared items (photos) can be automatically identified. With/without user- generated tags.

2. It proposes to use private photos in a privacy-preserving manner and social contexts to derive a personal Facial Recognition (FR) engine for any particular user.

Problem Statement for this project implementation would be Online sharing network users may be careless in posting contents and face the consequences; therefore the privacy protection becomes an important issue.

.Objectives or the main goal of the system is to design a secure photo sharing on Online Social Networks using Face Detection and Face Recognition.

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This section describe show photo sharing is securely done on a platform that is similar to facebook, application, and which is composed of

1) Web server 2) Database

Figure 1 represents the system architecture of picsec,i.e, the picture security management system which describes that the user can use the application created and can login into the application by providing their credentials. If the details are correct then they can access the application through the network, which will lead it to their homepage. The admin here can accept/reject the request of the users. The admin have all rights to change the details of the users who have registered

Figure 1:System Architecture III. IMPLEMENTATION

The application is divided into 6 modules. It contains user registration module, friends(management),posting process, face detection process, face recognition using RANSAC and SURF and approval system and decision making.

A. USER REGISTRATION PROCESS

Here user can register them self with basic information like user ID and mail ID and user name password and user image. Figure 2 here show the registration page of the project.

Figure 2: User Registration Process

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This module user can send friend request to another user if he/she is accept the friend request we are adding in friend list and they can create the friends group also.

C. POSTING PROCESS

This module user can send friend request to another user if he/she is accept the friend request we are adding in friend list and they can create the friends group also.

D. FACE DETECTION PROCESS

The OpenCV Technology is making use for detection of the face in the image. OpenCV will read our Image file to detect the Face region. OpenCV also comes with Implemented files for detecting profile faces. In Face detection module from the input image its crapping face region alone by using OpenCV. . Figure 4 explains the HoD functionalities.

E. FACE RECOGNITION USING RANSAC AND SURF

Finding the number of matching points using SURF detection Algorithm. After finding the matching point update in database. SURF is a re-sampling technique that generates candidate solutions by using the minimum number observations (data points) required to estimate the underlying model parameters. SURF uses the smallest set possible and proceeds to enlarge this set with consistent data points .SURF selects randomly the minimum number of points required to determine the model parameters and solve for the parameters of the model. It determine how many points from the set of all points fit with a predefined tolerance? If the fraction of the number of inliers over the total number points in the set exceeds a predefined threshold, re-estimate the model parameters using all the identified inliers and terminate. By using RANSAC algorithm we draw the matching point’s image between the two images.

Figure 4 explains the principal functionalities.

F. APPROVAL SYSTEM AND DECISION MAKING.

After comparing processes we can find Average matching Points between all the Dataset image that average will reach threshold and select the classification name and display user name..

IV. RESULTS a) Home page:

The main screen contains the login page the users can sign-up and login to the application.

Figure 6: home page

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The new users can fill up the details here.

Figure 7: registration page c) User profile:

The users can fill the form and apply for leave.

Figure 8: user profil screen d) Users friend request list:

Figure 9: User friend request list

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Figure 10: Accept the Friend request

IV.CONCLUSION

Photo sharing is one of the most popular features in online social networks such as Facebook. Unfortunately, careless photo posting may reveal privacy of individuals in a posted photo. To curb the privacy leakage, and enable individuals potentially in a photo to give the permissions before posting a co-photo. Here we have designed privacy-preserving Face recognition system to identify individuals in a co-photo. The proposed system is featured with low computation cost and confidentiality of the training set. Thereby the proposed scheme is very useful in protecting user’s privacy in photo/image sharing over online social networks.

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