IRJET- Recognizing User Portrait for Fraudulent Identification on Online Applications

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https://irjet.net/archives/V7/i3/IRJET-V7I3376.pdf

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1944
RECOGNIZINGUSER PORTRAIT FOR FRAUDULENTIDENTIFICATION ON
ONLINE APPLICATIONS
Antony Sophia1, Mukilan2, Guru Prasath3
1
Antony Sophia, Assistant Professor, Department of Computer Science and Engineering, Jeppiaar SRR Engineering
college, Chennai, India
2
Mukilan, Student, Department of Computer Science and engineering, Jeppiaar SRR Engineering College,
Chennai, India
3
Guru Prasath, Student, Department of Computer Science and engineering, Jeppiaar SRR Engineering
College, Chennai, India
-------------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - On-line Social Applications (OSAs) are
increasingly influencing the way people communicate with
each other and share personal, professional and political
information. Notable locales, for example, Facebook,
LinkedIn, Twitter, and Google+ have a great many clients
over the globe. With the wide ubiquity there are part of
security and protection dangers to the users of On-line Social
Applications (OSAs) such as breach of privacy, viral
marketing, structural attacks, malware attacks and Profile
Cloning. Informal communities have allowed individuals
have their own virtual characters which they use to
communicate with other online clients. It is likewise totally
conceivable and normal for a client to have more than one
online profile or even a totally unique mysterious online
personality. Now and again it is expected to expose the
namelessness of specific profiles, or to distinguish two
contrast profiles as having a place with a similar client.
Entity Resolution (ER) is the errand of coordinating two
distinctive online profiles conceivably from interpersonal
organizations. Solving ER has a identification of fake profiles.
Our solution compares profiles based similar attributes. The
system was tasked with matching two profiles that were in a
pool of extremely similar profiles.
Key Word: Online Social Application(OSA), Entity
Resolution(ER), Fake Profile, Profile Cloning, Privacy.
1.INTRODUCTION
Social Applications have permitted people have
their own virtual identities which they use to interact with
other online users. Social Applications such as Facebook,
Twitter and Google+ have attracted millions of users. One
of the most widely used Social Applications, Facebook,
recently had an initial public offering, which was among the
biggest in Internet technology. These Social Applications
allow real world people to create online profiles based on
the information they give. The profiles are online identities
that are capable of being totally independent of their real
life identity. The interaction between these profiles
happens through direct communication with other
users, publishing posts and pictures, expressing
opinions on other people’s content, etc. Each profile can
be seen as a node on a graph and the friendship
relations between profiles are the vertices, hence the
term social network. Such profiles are created during
the registration process. Since the registration process
for the average social network requires the user to
manually enter their information it is very easy and not
an uncommon occurrence to create a profile with fake
or erroneous information. It could be to the interest of
multiple parties to acquire the public information of
these profiles from different Social Applications to
correlate and match data in order to identify a single
entity with different profiles. This process of matching
profiles into a single entity representing one real world
entity is known as Entity Resolution. ER also has real
world uses such as the construction of a more detailed
source of information on people, searching for people
across different Social Applications, employers being
able to realize their worker up-and-comers more
before contracting them, improving advertising
methodologies, recognizing counterfeit profiles, and so
on. we present an alternative form of comparing
profiles that takes advantage of other information that
is available, without using training phase. To solve ER,
we went farther than just comparing image based
features between profiles; we also compared other
types of information if it was publically available. Image
based features such as the profile’s images and posted
images were compared with string comparison
methods that obtain best results.
2.RELATED WORK
In this segment we present the literature
survey of detecting fake profile in online social
applications. Table 1 represent the survey overview.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1945
AUTHOR TITLE DISADVANTAGES
Georgios DETECTING It doesn’t extend its
functionality to
utilize other
popular social
networks and
create a profile
parser for each
network
Kontaxis, Iasonas SOCIAL
Polakis, Sotiris NETWORK
Ioannidis, PROFILE
Evangelos P. CLONING
Markatos(2011)
Stephen Gould; DECOMPOSING A It doesn’t integrate
the method with
state-of-the-art
approaches that
reason more
explicitly about
depth or occlusion.
Richard Fulton; SCENE INTO
Daphne GEOMETRIC AND
Koller(2009) SEMANTICALLY
CONSISTENT
REGIONS
Olivier Duchenne AUTOMATIC The discriminative
; Ivan Laptev ; ANNOTATION OF model will need the
Josef Sivic ; HUMAN ACTIONS combination of
Francis Bach ;
Jean
Ponce(2009)
IN VIDEO multiple subtasks
for a solving
complex real-world
problem.
Fig -1: ArchitectureDiagram
The modules used in application is
described as follows
3.1. CREATION AND VERIFICATION OF USER DETAILS
2.1.CHALLENGESIN EXISTING WORK
No prior work has fully analyzed whether these
notions of romance introduce traits that could be leveraged
to build a detection system. Simple categorical information
relating to the user, such as age, gender, ethnicity only has
been used but it wasn’t providedan effective result.
The short textual self-description from the user, in
which they advertise their key traits and interests are also
results in failure. There is possibility of misuse of account
and photos by another user. Once the account has been
mistakenit is consideredas cybercrime.
3.IMPLEMENTATION
The proposed system detects profile cloning in the
same site. The technique uses Steganography in which an
id is added to the profile pictures and posted pictures. The
id will be an email id of the user which is added to the image
while uploading. If another user tries to create a profile
using the images from another user profile, notification will
be sent to that user. No other account can’t be able to create
on the same user name. If the user tries to create fake
account, notificationwill be send to respective user.
This module presents site visitors with a form
with username and password fields. In the event that the
client enters a substantial username and secret word they
will be allowedaccess to extra assets on your site.
Fig -2: Creation Page
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1946
3.2. USING STEGANOGRAPHY TO HIDE AN ID IN
THE USER’S PHOTOS
This module consists of a new stenographic
algorithm for hiding data in images. Steganography is the
act of concealingsecret message inside any media.
Most data hiding systems take advantage of human
perceptual weaknesses we have tested few images with
different sizes of data to be hidden and concluded that the
resultingsteno imagesdo not have any noticeablechanges.
Once another user who downloads the image
cannot see the image as it is hidden. We have likewise
utilized water mark strategies that won't be noticeable in
any event, for the clients.
Fig -3: Uploading Page
3.3 MATCHING OF UPLOADED PICTURE WITH
THE DATABASE
This module checks the picture uploaded during
profile creation. The user can download an image but they
cannot upload the same image due to the hidden data inside
each photo. When another user creates an account using the
pictures from another user, it will be immediately flagged
as an existing profile picture and therefore preventing the
user from creatinga fake profile.
Fig -4: Data flow diagram for matching process
3.4. NOTIFYING THE ORIGINAL USER
This module informs the original user a fake profile
implicating him is being created. The user can then verify
whether it is him creating a duplicate profile or someone
else creating fake profile of him. The original user can
then prevent or allow the profile creation. A notification
via a text message or mail is sent.
Fig -5: NotificationProcess
4.CONCLUSION
We solved Entity Resolution with our system
and used it to compare online user profiles from social
applications in order to identify cloned profiles. Our
systems compare two images and identify them as fake
or not. We are using Steganography Algorithm that
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1947
hides the information inside the image. In this way new images that
are uploaded in our site are compare to the existing user profiles. If
the image is identified as an existing image then a notification is sent
to original user. The original user allows the uploading, then images
was uploadedotherwise blocked.
REFERENCES
[1] J. T. Hancock, L. Curry, S. Goorha, and M. Woodworth Automated
linguistic analysis of deceptive and truthful synchronous
computer-mediated communication. In System Sciences, 2005.
HICSS’05. Proceedings of the 38th Annual Hawaii International
Conference on, page 22c. IEEE, 2005.
[2] A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for
generating image descriptions. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition, pages
3128–3137,2015.
[3] G. Kontaxis, I. Polakis, S. Ioannidis, and E. P. Markatos.
Detecting social network profile cloning. In Pervasive Computing
and Communications Workshops (PERCOM Workshops), 2011 IEEE
International Conference on, pages 295–300.IEEE, 2011.
[4] J. Donahue, L. Anne Hendricks, S. Guadarrama, M.
Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term
recurrent convolutional networks for visual recognition and
description. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition,pages 2625–2634,2015
[5] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature
hierarchies for accurate object detection and semantic
segmentation. In Proceedings of the IEEE conference on
Computer Vision and Pattern Recognition,2014.
[6] S. Gould, R. Fulton, and D. Koller. Decomposing a scene into
geometric and semantically consistent regions. In Computer
Vision, 2009 IEEE 12th International Conference on, pages 1–8.
IEEE, 2009.
[7] S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for
human action recognition,” in IEEE Trans. Pattern Anal. Mach.
Intell., 2013.
[8] Y. Wang and G. Mori, “Max-Margin Hidden Conditional Random
Fields for Human Action Recognition,” Proc. IEEE Conf. Computer
Vision and Pattern Recognition, pp. 872-879,2009.
[9] O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, “Automatic
Annotation of Human Actions in Video,” Proc. 12th IEEE Int’l Conf.
Computer Vision, pp. 1491-
1498, 2009.
[10] M. Ranzato, F. Huang, Y. Boureau, and Y. LeCun, “Unsupervised
Learning of Invariant Feature Hierarchies with Applications to
Object Recognition,” Proc. IEEE Conf. Computer Vision and Pattern
Recognition,2007.

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IRJET- Recognizing User Portrait for Fraudulent Identification on Online Applications

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1944 RECOGNIZINGUSER PORTRAIT FOR FRAUDULENTIDENTIFICATION ON ONLINE APPLICATIONS Antony Sophia1, Mukilan2, Guru Prasath3 1 Antony Sophia, Assistant Professor, Department of Computer Science and Engineering, Jeppiaar SRR Engineering college, Chennai, India 2 Mukilan, Student, Department of Computer Science and engineering, Jeppiaar SRR Engineering College, Chennai, India 3 Guru Prasath, Student, Department of Computer Science and engineering, Jeppiaar SRR Engineering College, Chennai, India -------------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - On-line Social Applications (OSAs) are increasingly influencing the way people communicate with each other and share personal, professional and political information. Notable locales, for example, Facebook, LinkedIn, Twitter, and Google+ have a great many clients over the globe. With the wide ubiquity there are part of security and protection dangers to the users of On-line Social Applications (OSAs) such as breach of privacy, viral marketing, structural attacks, malware attacks and Profile Cloning. Informal communities have allowed individuals have their own virtual characters which they use to communicate with other online clients. It is likewise totally conceivable and normal for a client to have more than one online profile or even a totally unique mysterious online personality. Now and again it is expected to expose the namelessness of specific profiles, or to distinguish two contrast profiles as having a place with a similar client. Entity Resolution (ER) is the errand of coordinating two distinctive online profiles conceivably from interpersonal organizations. Solving ER has a identification of fake profiles. Our solution compares profiles based similar attributes. The system was tasked with matching two profiles that were in a pool of extremely similar profiles. Key Word: Online Social Application(OSA), Entity Resolution(ER), Fake Profile, Profile Cloning, Privacy. 1.INTRODUCTION Social Applications have permitted people have their own virtual identities which they use to interact with other online users. Social Applications such as Facebook, Twitter and Google+ have attracted millions of users. One of the most widely used Social Applications, Facebook, recently had an initial public offering, which was among the biggest in Internet technology. These Social Applications allow real world people to create online profiles based on the information they give. The profiles are online identities that are capable of being totally independent of their real life identity. The interaction between these profiles happens through direct communication with other users, publishing posts and pictures, expressing opinions on other people’s content, etc. Each profile can be seen as a node on a graph and the friendship relations between profiles are the vertices, hence the term social network. Such profiles are created during the registration process. Since the registration process for the average social network requires the user to manually enter their information it is very easy and not an uncommon occurrence to create a profile with fake or erroneous information. It could be to the interest of multiple parties to acquire the public information of these profiles from different Social Applications to correlate and match data in order to identify a single entity with different profiles. This process of matching profiles into a single entity representing one real world entity is known as Entity Resolution. ER also has real world uses such as the construction of a more detailed source of information on people, searching for people across different Social Applications, employers being able to realize their worker up-and-comers more before contracting them, improving advertising methodologies, recognizing counterfeit profiles, and so on. we present an alternative form of comparing profiles that takes advantage of other information that is available, without using training phase. To solve ER, we went farther than just comparing image based features between profiles; we also compared other types of information if it was publically available. Image based features such as the profile’s images and posted images were compared with string comparison methods that obtain best results. 2.RELATED WORK In this segment we present the literature survey of detecting fake profile in online social applications. Table 1 represent the survey overview.
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1945 AUTHOR TITLE DISADVANTAGES Georgios DETECTING It doesn’t extend its functionality to utilize other popular social networks and create a profile parser for each network Kontaxis, Iasonas SOCIAL Polakis, Sotiris NETWORK Ioannidis, PROFILE Evangelos P. CLONING Markatos(2011) Stephen Gould; DECOMPOSING A It doesn’t integrate the method with state-of-the-art approaches that reason more explicitly about depth or occlusion. Richard Fulton; SCENE INTO Daphne GEOMETRIC AND Koller(2009) SEMANTICALLY CONSISTENT REGIONS Olivier Duchenne AUTOMATIC The discriminative ; Ivan Laptev ; ANNOTATION OF model will need the Josef Sivic ; HUMAN ACTIONS combination of Francis Bach ; Jean Ponce(2009) IN VIDEO multiple subtasks for a solving complex real-world problem. Fig -1: ArchitectureDiagram The modules used in application is described as follows 3.1. CREATION AND VERIFICATION OF USER DETAILS 2.1.CHALLENGESIN EXISTING WORK No prior work has fully analyzed whether these notions of romance introduce traits that could be leveraged to build a detection system. Simple categorical information relating to the user, such as age, gender, ethnicity only has been used but it wasn’t providedan effective result. The short textual self-description from the user, in which they advertise their key traits and interests are also results in failure. There is possibility of misuse of account and photos by another user. Once the account has been mistakenit is consideredas cybercrime. 3.IMPLEMENTATION The proposed system detects profile cloning in the same site. The technique uses Steganography in which an id is added to the profile pictures and posted pictures. The id will be an email id of the user which is added to the image while uploading. If another user tries to create a profile using the images from another user profile, notification will be sent to that user. No other account can’t be able to create on the same user name. If the user tries to create fake account, notificationwill be send to respective user. This module presents site visitors with a form with username and password fields. In the event that the client enters a substantial username and secret word they will be allowedaccess to extra assets on your site. Fig -2: Creation Page
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1946 3.2. USING STEGANOGRAPHY TO HIDE AN ID IN THE USER’S PHOTOS This module consists of a new stenographic algorithm for hiding data in images. Steganography is the act of concealingsecret message inside any media. Most data hiding systems take advantage of human perceptual weaknesses we have tested few images with different sizes of data to be hidden and concluded that the resultingsteno imagesdo not have any noticeablechanges. Once another user who downloads the image cannot see the image as it is hidden. We have likewise utilized water mark strategies that won't be noticeable in any event, for the clients. Fig -3: Uploading Page 3.3 MATCHING OF UPLOADED PICTURE WITH THE DATABASE This module checks the picture uploaded during profile creation. The user can download an image but they cannot upload the same image due to the hidden data inside each photo. When another user creates an account using the pictures from another user, it will be immediately flagged as an existing profile picture and therefore preventing the user from creatinga fake profile. Fig -4: Data flow diagram for matching process 3.4. NOTIFYING THE ORIGINAL USER This module informs the original user a fake profile implicating him is being created. The user can then verify whether it is him creating a duplicate profile or someone else creating fake profile of him. The original user can then prevent or allow the profile creation. A notification via a text message or mail is sent. Fig -5: NotificationProcess 4.CONCLUSION We solved Entity Resolution with our system and used it to compare online user profiles from social applications in order to identify cloned profiles. Our systems compare two images and identify them as fake or not. We are using Steganography Algorithm that
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1947 hides the information inside the image. In this way new images that are uploaded in our site are compare to the existing user profiles. If the image is identified as an existing image then a notification is sent to original user. The original user allows the uploading, then images was uploadedotherwise blocked. REFERENCES [1] J. T. Hancock, L. Curry, S. Goorha, and M. Woodworth Automated linguistic analysis of deceptive and truthful synchronous computer-mediated communication. In System Sciences, 2005. HICSS’05. Proceedings of the 38th Annual Hawaii International Conference on, page 22c. IEEE, 2005. [2] A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3128–3137,2015. [3] G. Kontaxis, I. Polakis, S. Ioannidis, and E. P. Markatos. Detecting social network profile cloning. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 295–300.IEEE, 2011. [4] J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pages 2625–2634,2015 [5] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition,2014. [6] S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1–8. IEEE, 2009. [7] S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for human action recognition,” in IEEE Trans. Pattern Anal. Mach. Intell., 2013. [8] Y. Wang and G. Mori, “Max-Margin Hidden Conditional Random Fields for Human Action Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 872-879,2009. [9] O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, “Automatic Annotation of Human Actions in Video,” Proc. 12th IEEE Int’l Conf. Computer Vision, pp. 1491- 1498, 2009. [10] M. Ranzato, F. Huang, Y. Boureau, and Y. LeCun, “Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition,2007.