Contents media and do not want to come

Contents
Project
Title. 2
Introduction. 2
Problem
Statement 3
Literature
review.. 5
Project
Aims & Objectives. 6
Aims. 6
Objectives. 6
Research
Questions. 6
Domain. 6
Technical 7
Proposed
Work Plan for Level 3, Semester 1. 7
References. 8
 

 

 

 

 

 

 

 

 

 

 

Project Title

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BeGone: Safe Filter for Social Media

Introduction

The postings of content not suitable
for everyone’s views on social media has been a huge problem since the very
existence of social media itself. Today, it is very easy for anyone to post
literally anything on their social media page and somehow manages to avoid
being detected by the social media’s security checks. This is a big issue for
those who wish to use social media for the sake of keeping in touch with
friends and families who are far away and to even catch up on the latest news
and happenings around the world.

Most people however, do not consider
this issue to be a big one. They shrug off all discussions and advise people to
stay away from social media or to not even use it in the first place. The
problem of indecent postings in social media has to be tackled as one would not
simply tell a child to not go to school if they were bullied. They would solve
the problems to ensure the child has a safe experience in school.

Similarly, BeGone is proposed to
ensure a safe experience for people who use social media and do not want to
come into contact with such media. BeGone is a safe filter for social media
that when activated by a user, will block out completely, any media or text
that is deemed indecent. This filter will not be a mandatory inclusion in all
social media, instead it will be more like a browser extension so that a user
can decide if they want to turn it on or leave it disabled.

 

 

 

 

 

 

 

 

Problem Statement

Social media plays an important role
in people’s lives these days. Almost everyone has a social media account.
Figure 1 below shows the percentage of people having a social media account in
the United States alone.

Figure 1: The percentage of people
having a social media account in US according to The Statistics Portal.

According to The Statistics Portal,
the percentage of people having a social media account in the US was 81% in
2017, a 3% increase since 2016 (Statista, 2017). This statistic will
be likely to be on the rise as the years go by.

In fact, each individual may have
multiple accounts across different social media platforms. People use social
media for many reasons, from keeping in touch with friends and families who are
at a distance to even getting updated on the latest news and development
worldwide. However it is possible that a user may bump into indecent content
every once in a while. Such content can range from gory and bloody killings to
criminals live streaming their crimes, pornographic videos to rape, graphic
images to animal abuse videos and so on. This is dangerous especially for the
young people who come into contact with such content as it may affect them
emotionally and may even encourage them to do things that are morally
incorrect.

 

 

Such indecent contents may even be
sent by someone the user knows with intention to cause harm. This can lead to
cyberbullying where a handful of victims would result to ending their own
lives. Fcebook, a social media giant, has implemented a system that
automatically hides media that are suspected to be unsuitable for viewing.
However the filter includes a function where the user can click a button to
uncover the hidden content. That function can be wrongly used and manipulated
for people’s own needs. This is where BeGone will be introduced to prevent such
incidents. BeGone is a safe filter for social media that ensures social media
users to be unable to see any indecent media or messages as they browse through
their social media page, when the filter is turned on. This works by using
digital image processing techniques which converts a media into black and white
display. The media will then be converted into binary form and from there the
binary pattern can be cross referenced with binary samples in the ever-growing
database to match them and find out whether the media is safe or if it is not
suitable for viewing. If the media is not suitable for viewing, BeGone will
then block out that media completely off a user’s social media account if the
filter is activated by the user. BeGone also works for texts messages that are
vulgar or not suitable, or if the user does not want to come into contact with
such texts, be it in postings by other users or even in chatboxes. This happens
by using wordfilter algorithms that is already being used in the gaming
industry in their online parties and chat rooms. The wordfilter algorithm can
be implemented in BeGone to check the text being published and if an unsuitable
word is detected, BeGone will censor the text with symbols or even block it
completely, depending on the user preference.

 

 

 

 

 

 

Literature review

(What are the common methods used to
design an image cross referencing system?)

 

The problem of using social media
for the purpose of spreading indecent content has been around since the very
introduction of social media to the public. According to Benjamin Hale, one of
the authors of History Coooperative, by the year 2000, around 100 million
people in the world had access to the Internet and had social media accounts (Hale, 2016). Social media sites
such as Six Degrees (1997-2001) and Myspace (2000) were the pioneers of social
media. The social media that shocked the term “social media” was Facebook since
its launch for the public back in 2005. Even since the launch, Facebook has
been used to deliver inappropriate content and messages that can hurt people,
whether intentionally or not.

 

In light of said events, these
issues have to be solved. The problems can be solved by preventing indecent
content from even being displayed. As stated by Gregory Sean Cox in his journal
Designing Hypothesis Tests for Digital Image Matching (2000), the problem of
matching two pictures can be executed by using digital image processing techniques
to match media and determine if it is suitable to be displayed. Digital image
processing is a highly suitable method because it works by accessing the error
rate execution at the beginning, over multiple conditions using Monte
Carlo techniques in conjunction with a system for producing random picture
sets. Even with deviations from the expected model, the
ideal content displays a critical execution advantage over tests
based on traditional methods. Therefore it is an improvement over using
traditional methods (Cox, 2000).

 

Light plays an important part in an
image’s structure. William K. Pratt states in his book Digital Image Processing
3rd Edition (2001), that there are three main indicators of the
amount of light a digital image has. They are the image brightness, hue
strength and level of saturation. These factors are important in determining
the nature of the objects present in the image, which will play a vital role in
determining if the object is suitable to be displayed in one’s social media
page (Pratt, 2001).

 

 

 

Project Aims & Objectives

Aims

The aim of
BeGone is to ensure a safe social media experience for teenagers and assurance
to parents that their children are not exposed to any indecent content online
and are safe when using social media by preventing indecent media and texts
being displayed on a user’s profile when activated by said user.

 

Objectives

u Prevent
users from accessing links that have potential to display indecent content

u Prevent
users from sending or receiving vulgar messages by using wordfilter to ensure
all text messages are clean.

u To block
out all inappropriate images, text, and videos off a certain user’s profile
when activated with digital image processing algorithms for media and
wordfilter algorithms for texts

u To cross
reference all media with the ever-growing database to ensure that the media and
texts displayed on the user’s profile are safe for viewing with binary pattern
matching.

 

Research Questions

 

Domain

1.     What
are the common methods used to design an image cross referencing application
and is said method suitable for use in this project?

 

2.     What
are the pros and cons of using the selected method and do the pros outweigh the
cons?

 

3.     How
will this project benefit the ever-growing social media industry and their
users and does it also take a negative impact on both parties?

 

Technical

1.     How
accurate is image binary matching and wordfilter, and will the techniques make
errors once deployed?

 

2.     What
are the alternatives available in the case of the current techniques not being
sufficient to solve the problems at hand?

 

 

3.     How
much maintenance is required, and will the maintenance be hard and time
consuming?

 

Proposed Work Plan for Level 3, Semester 1.

Activity

Week

1

2

3

4

5

6

7

8

9

10

11

12

13

14

 

Introduction

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Literature Review

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

System Development Methodology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Primary Research

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Secondary Research

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Requirements Development

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Requirements Validation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Reflection & Conclusion

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Review & Documentation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Cox, G. S., 2000. Digital
Image Matching Techniques. Designing Hypothesis Tests for Digital Image
Matching, p. 3.
Hale, B., 2016. The History of Social Media: Social
Networking Evolution!. Online
Available at: http://historycooperative.org/the-history-of-social-media/
Accessed January 2018.
Pratt, W. K., 2001. Digital Image Processing. Third
ed. Los Altos, California: Wiley-Interscience.
Statista, 2017. The Statistics Portal. Online
Available at: https://www.statista.com/statistics/273476/percentage-of-us-population-with-a-social-network-profile/
Accessed December 2017.