B13 FIRST REVIEW 2 (1).pdf advanced machine learning

It an pdf which is used to know about the detection of fake profiles in digital media.

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
•LITERATURESURVEY
•BASEPAPER
•PROPOSEDWORK

3. ABSTRACT/OBJECTIVE
Theproliferationoffakeprofilesandmanipulatedimagesonsocialmediaplatformsposesasignificant
challengetoonlineauthenticityandtrustworthiness.Inthisproject,weproposeamachinelearning-
basedapproachtodetectfakeprofilesandfakeimageswithhighaccuracy.Ourmethodologyinvolves
collectingalargedatasetofbothrealandfakeprofiles/images,extractingrelevantfeatures,andtraining
arobustclassificationmodel.Weleverageadvancedtechniquessuchasdeeplearningforimage
analysisandnaturallanguageprocessingforprofiletextanalysis.Additionally,weexploreensemble
learningmethodstofurtherimprovedetectionperformance.Throughextensiveexperimentationand
evaluationondiversedatasets,wedemonstratetheeffectivenessofourapproachinaccurately
identifyingfakeprofilesandimages,therebycontributingtothemitigationofonlinemisinformation
anddeception.

4. LITERATURE SURVEY
Author Paper Title MethodologyTechniques and Tools UsedDrawbacks
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 accesto
user click behavior
data
Stringhiniet
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, LogisticRegression
Limited to detecting
temporal patterns of
spamming behavior,
may not generalize to
all types
offakeaccounts.
Cresciet al.(2015)“fame for sale: efficient
detection of fake Twitter
followers”
Network AnalysisGraph-based techniques,
Bot detection algorithms,
Statisticalanalysis.
Requires access to
ground truth data for
training, Limited to
detecting fake
followers rather than
broaderfakeaccounts.
Shao et al.(2018)“FakeNewsNet: A Data
Repistorywith news
content and social
context and dynamic
information for studying
fake news on social
media”
Content AnalysisNatural Language
processing (NLP)
techniques,Deep
learning(e.g.,CNN,LSTM),S
ocial network analysis
Relies on labeled
datasets,Limitedto
fake news detection
rather than fake
accounts

6. Overview:-
Theexistingsystemisamachinelearning-basedapproachtodetectfakeTwitteraccounts.
KeyFeatures:
1.DataFeatures:Utilizesarangeoffeatureslikethenumberofabusereports,rejected
friendrequests,unacceptedfriendrequests,numberoffriendsandfollowers,likesto
unknownaccounts,andcommentsperday.
2.MachineLearningModels:EmploysclassifierssuchasNaiveBayes,SVC(Support
VectorClassifier),andK-NearestNeighbors.
3.EvaluationMetrics:Focusesoncalculatingaccuracyanderrorrateoftheclassifiers.
4.ResultVisualization:Providesabargraphcomparisonoftheperformanceofdifferent
classifiers.
Existing System

7. Limitations
DataImbalance
FeatureEngineeringChallenges
AdversarialAttacks
GeneralizationIssues
PrivacyConcerns

8. PROPOSED SYSTEM
Overview
Anadvancedsystemintegratingmoredynamicfeaturesandreal-timeanalysistoenhance
thedetectionoffakeTwitteraccounts.
Enhancements
1.DynamicFeatureAnalysis
2.AdvancedMachineLearningModels
3.Real-TimeDetection
4.AdaptiveLearning
5.ExplainabilityandTransparency

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

10. ExpectedImprovements
•Enhancedaccuracyandadaptabilitytonewtypesoffakeaccountbehaviors.
•Real-timedetectioncapabilityleadingtoquickerresponseandmitigation.
•Greatertransparencyandtrustinthedetectionsystem.
Thisproposedsystemaimstoaddressthelimitationsoftheexistingonebyincorporating
advancedtechnologiesandmethodologies.Thefocusisoncreatingamorerobust,
adaptable,anduser-friendlysystemthatkeepspacewiththerapidlyevolvinglandscape
ofsocialmediaandonlinebehaviors

11. 70% COMPLETION OUTPUT

12. ADVANTAGES
ImprovedOnlineTrustworthiness
MitigationofMisinformation
EnhancedUserSecurity
ProtectionofBrandReputation
CostandResourceSavingsEmpowermentofUsers
InsightsintoOnlineBehavior

13. APPLICATIONS:
SocialMediaPlatforms
OnlineMarketplace
Cybersecurity
DigitalForensics
BrandProtection
ContentModeration
JournalismandMedia
AcademicResearch

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:
1.P. Kumar, M. Vatsa, and R. Singh, ‘‘Detecting Face2Face facial reenactment in videos,’’ in Proc. IEEE Winter
Conf. Appl. Comput. Vis. (WACV), Mar. 2020, pp. 2578–2586
2.K.Carta, C. Barral, N. El Mrabet, and S. Mouille, ‘‘Video injection attacks on remote digital identity verification
solution using face recognition,’’ in Proc. 13th Int. Multi-Conference Complex., Informat. Cybern. (IMCIC), Mar.
2022, pp. 92–97.
3.T.-L. Do, M.-K. Tran, H. H. Nguyen, and M.-T. Tran, ‘‘Potential attacks of DeepFakeon eKYCsystems and
remedy for eKYCwith DeepFakedetection using two-stream network of facial appearance and motion
features,’’ Social Netw. Comput. Sci., vol. 3, no. 6, pp. 1–17, Sep. 2022.
4.A. Nanda, S. W. A. Shah, J. J. Jeong, R. Doss, and J. Webb, ‘‘Towards higher levels of assurance in remote
identity proofing,’’ IEEE Consum. Electron. Mag., vol. 13, no. 1, pp. 1–8, Jan. 2023.
5.D. Dagarand D. K. Vishwakarma, ‘‘A literature review and perspectives in deepfakes: Generation, detection,
and applications,’’ Int. J. Multimedia Inf. Retr., vol. 11, no. 3, pp. 219–289, Sep. 2022.

15. 6.A.Rössler, D.Cozzolino, L.Verdoliva, C. Riess, J. Thies, and M. Niessner,
‘‘FaceForensics++:Learningtodetectmanipulatedfacialimages,’’inProc. IEEE/CVF Int. Conf. Comput. Vis.
(ICCV), Oct. 2019, pp. 1–11.
7.R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, ‘‘High-resolution image synthesis with latent
diffusion models,’’ in Proc. IEEE/CVF Conf. Com
8.N. Dufour, A. Gully, P. Karlsson, A. V. Vorbyov, T. Leung, J. Childs, and C. Bregler, ‘‘DeepFakesdetection
dataset by Google & Jigsaw,’’ Google, USA, 2019. [Online]. Available: https://blog.research.google/
2019/09/contributing-data-to-deepfake-detection.html?m=1

16. THANK YOU