The document presents a project from Sri Venkateswara College focusing on using advanced machine learning methods to detect fake accounts and images on digital media. The proposed system enhances existing methodologies by integrating dynamic features, real-time detection, and improved classification models to address limitations such as data imbalance and privacy concerns. The ultimate goal is to bolster online trust and mitigate misinformation across various applications, including social media and cybersecurity.

1. SRI VENKATESWARA COLLEGE OF ENGINEERING AND TECHNOLOGY
CHITTOOR(AUTONOMOUS)
BACHELOR OF TECHNOLOGY
IN
ELECTRONICS AND COMMUNICATION ENGINEERING
Title of the project
ADVANCED MACHINE LEARNING SOLUTIONS FOR DETECTING
FAKE ACCOUNT IN DIGITAL MEDIA
BATCH NO: B13
20781A04D1 : P.LOKESH
20781A04G1 :S.IRFAN
20781A04D5:P.VIJAY BHASKAR REDDY
20781A04G2: S.IRFAN
20781A04C7:P.MAHENDRA
Under the Guidance of
Mrs.M.SUMATHI,
ASSOCIATE PROFESSOR

2. CONTENTS
• OBJECTIVE/ABSTRACT
• INTRODUCTION
• LITERATURE SURVEY
• BASE PAPER
• PROPOSED WORK

3. ABSTRACT/OBJECTIVE
The proliferation of fake profiles and manipulated images on social media platforms poses a significant
challenge to online authenticity and trustworthiness. In this project, we propose a machine learning-
based approach to detect fake profiles and fake images with high accuracy. Our methodology involves
collecting a large dataset of both real and fake profiles/images, extracting relevant features, and training
a robust classification model. We leverage advanced techniques such as deep learning for image
analysis and natural language processing for profile text analysis. Additionally, we explore ensemble
learning methods to further improve detection performance. Through extensive experimentation and
evaluation on diverse datasets, we demonstrate the effectiveness of our approach in accurately
identifying fake profiles and images, thereby contributing to the mitigation of online misinformation
and deception.

4. LITERATURE SURVEY
Author Paper Title Methodology Techniques and Tools Used Drawbacks
Cheng at al.(2018) “You Are How You
Click: Clickstream
Analysis For Sybil
Detection”
Clickstream
analysis
Graph based techniques,
Machine Learning
Classifiers(e.g., Random
Forest)
Requires acces to
user click behavior
data
Stringhini et
al.(2010)
“Detecting Spammers on
Social Networks”
Analysis of
Network
Structure
Graph based techniques,
Machine Learning
Classifiers(e.g. SVM)
Limited Scalability for
real-time detection
Lee et al.(2016) “Detecting Suspicious
Accounts in Online Social
Networks”
Behavioral
analysis
User behavioral features,
Machine Learning classifiers
(e.g.,RandomForest, XGBoost)
Limited Scalability for
real-time detection
Wang et al.(2019) “Detecting Fake Accounts
in Online Social Networks
at Scale”
Network
Analysis
Graph based techniques,
Machine Learning
(e.g.,CNN,LSTM),Behavioral
analysis
Limited Specific
Social network
platforms,
Performance
overhead for large-
scale analysis

5. Author Paper Title Methodology Techniques and Tools
Used
Drawbacks
Kumar et al.(2017) “Temporal Patterns of
User Behavior on Twitter
and Diurnal variation of
social spam”
Time-based
analysis
Time-series analysis,
Machine Learning
classifiers (e.g., Decision
Trees, Logistic Regression
Limited to detecting
temporal patterns of
spamming behavior,
may not generalize to
all types
of fake accounts.
Cresci et al.(2015) “fame for sale: efficient
detection of fake Twitter
followers”
Network Analysis Graph-based techniques,
Bot detection algorithms,
Statistical analysis.
Requires access to
ground truth data for
training, Limited to
detecting fake
followers rather than
broader fake accounts.
Shao et al.(2018) “FakeNewsNet: A Data
Repistory with news
content and social
context and dynamic
information for studying
fake news on social
media”
Content Analysis Natural Language
processing (NLP)
techniques,Deep
learning(e.g.,CNN,LSTM),S
ocial network analysis
Relies on labeled
datasets,Limited to
fake news detection
rather than fake
accounts

6. Overview : -
The existing system is a machine learning-based approach to detect fake Twitter accounts.
Key Features:
1. Data Features: Utilizes a range of features like the number of abuse reports, rejected
friend requests, unaccepted friend requests, number of friends and followers, likes to
unknown accounts, and comments per day.
2. Machine Learning Models: Employs classifiers such as Naive Bayes, SVC (Support
Vector Classifier), and K-Nearest Neighbors.
3. Evaluation Metrics: Focuses on calculating accuracy and error rate of the classifiers.
4. Result Visualization: Provides a bar graph comparison of the performance of different
classifiers.
Existing System

7. Limitations
 Data Imbalance
 Feature Engineering Challenges
 Adversarial Attacks
 Generalization Issues
 Privacy Concerns

8. PROPOSED SYSTEM
Overview
An advanced system integrating more dynamic features and real-time analysis to enhance
the detection of fake Twitter accounts.
Enhancements
1. Dynamic Feature Analysis
2. Advanced Machine Learning Models
3. Real-Time Detection
4. Adaptive Learning
5. Explainability and Transparency

9. BLOCK DIAGRAM
DataSet
SVC, NAÏVE BAYES,
K Nearest neighbour
Fake ID
Normal ID
Reduction
Preprocessing
Classification result
Training Phase

10. Expected Improvements
• Enhanced accuracy and adaptability to new types of fake account behaviors.
• Real-time detection capability leading to quicker response and mitigation.
• Greater transparency and trust in the detection system.
This proposed system aims to address the limitations of the existing one by incorporating
advanced technologies and methodologies. The focus is on creating a more robust,
adaptable, and user-friendly system that keeps pace with the rapidly evolving landscape
of social media and online behaviors

11. 70% COMPLETION OUTPUT

12. ADVANTAGES
 Improved Online Trustworthiness
 Mitigation of Misinformation
 Enhanced User Security
 Protection of Brand Reputation
 Cost and Resource Savings Empowerment of Users
 Insights into Online Behavior

13. APPLICATIONS:
 Social Media Platforms
 Online Marketplace
 Cybersecurity
 Digital Forensics
 Brand Protection
 Content Moderation
 Journalism and Media
 Academic Research

14. Base paper :
HICHEM FELOUAT JUNICHI YAMAGISHI 1,2, HUY H. NGUYEN 1,2, (Member, IEEE), TRUNG-NGHIA LE 1,5, (Senior
Member, IEEE), AND ISAO ECHIZEN 1,2,6, (Senior Member, IEEE)
accepted 15 February 2024, date of publication 23 February 2024, date of current version 1 March 2024.
Reference Paper:
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15. 6. A.Rössler, D.Cozzolino, L.Verdoliva, C. Riess, J. Thies, and M. Niessner,
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2019/09/contributing-data-to-deepfake-detection.html?m=1

16. THANK YOU