Machine Learning - Presentation Template.pptx

VERIFEYE
Prof. Dr. Muhammad Sultan Zia
Supervisor
Fake AI review detector
Detecting AI-Generated Reviews Using a Pre-trained BERT Model

www.machinelearning.com
2
Group
Members
Handled the modeling and
training components
Marriam Ilyas
2020-Uet-Nfc-bsc- cs- 01
Handled the website development
Hafsa Hammad
2020-Uet-Nfc-bsc- cs- 05

Introduction
Verifeye is a cutting-edge tool
designed to detect AI-generated
reviews, leveraging the power of a
pre-trained BERT model to ensure
authenticity and integrity in online
reviews.
Overview
3

Challenges Posed by AI-Generated
Reviews
4
Consumer Misinformation
AI-generated reviews pose significant challenges in maintaining the authenticity and trustworthiness of online
platforms. The sophisticated nature of these reviews makes it difficult to distinguish them from genuine ones,
necessitating advanced detection methods.
Market Manipulation
Reputation Damage
Security Risks

5
Consumer Trust
Importance of Detecting
AI-Generated Reviews
Market Fairness
Security

6
Applications
E-commerce
Platforms
Online Services
Social Media

AUTHOR
Jacob Devlin, Ming-Wei Chang, Kenton Lee,
Kristina Toutanova
2019
2019
2023
BERT: PRE-TRAINING OF DEEP
BIDIRECTIONAL TRANSFORMERS FOR
LANGUAGE UNDERSTANDING
MACHINE LEARNING TECHNIQUE
BASED FAKE NEWS DETECTION
AUTHOR
Biplob Kumar Sutradhar1, Md. Zonaid1, Nushrat Jahan
Ria1 and Sheak Rashed Haider Noori1
TESTING OF DETECTION TOOLS FOR
AI-GENERATED TEXT
AUTHOR
Debora Weber-Wulf1 , Alla Anohina-Naumeca2 , Sonja
Bjelobaba3* , Tomáš Foltýnek4 , Jean Guerrero-Dib5 ,
Olumide Popoola6 , Petr Šigut4 and Lorna Waddington
TITLE Literature Review

2019
2019
2023
AI-GENERATED TEXT
TITLE
METHODOLOGY
Spotting fake news online needs well-cleaned data for
machine learning tools (like Naive Bayes). This study pre-
processed data before applying these tools (details in
data preprocessing section).
This study tests how well AI text-detection tools work.
They built diverse test cases (human-written, translated,
AI-generated, and edited AI text). They then evaluated 12
free detection tools.
BERT advances language tasks by using a unique pre-
training method (masked language modeling) that
considers both left and right context. This makes BERT
bidirectional and powerful, achieving top performance on
many NLP tasks.
Literature Review

2019
2019
2023
AI-GENERATED TEXT
TITLE FINDINGS
Social media fake news is a serious problem impacting
decisions. This study tested models for detection, with
Naive Bayes showing some promise (32% accuracy) but
aiming for better solutions in the future.
Current AI tools for finding AI-generated plagiarism are
flawed (inaccurate, easy to trick). They shouldn't be
used. Instead, educators should focus on preventing
plagiarism and improving assessment methods.
Unlabeled data + pre-trained language models = big
improvements in NLP tasks (even low-resource ones).
This study expands these benefits to bidirectional models,
creating a single powerful NLP tool.
Literature Review

10
Objectives
Develop Verifeye, an advanced tool capable of accurately
detecting AI-generated reviews using a Transfer Learning
Technique
Secondary Objectives
Utilize Pre-Trained Model
Performance Evaluation
User Interface Design

11
Methodology
Pre-
Trained
Model
Google Bert Base
Uncased
Training
Process
• Loss function
• Optimizer
Fined
Tuned
Bert
Model
Finalized
Model
Testing
Flask
Web
App
Setup
Final
Product
Hosting Model
at AWS
Thesis
Integration
Of Model With Web
Application

12
Data Collection
Thesis
Data Sets
Used
Fake Review Data Set
• 40 K Reviews
• 20 K Training
• 20 K Testing
IMDB Data Set
We built our dataset by
merging human reviews
from IMDB with another
dataset of labeled
computer-generated and
human-written reviews.
• 50 K Reviews
• 25 K Training
• 25 K Testing

13
Data Collection
Thesis
Fake Review Data
Set
balanced dataset of real and
AI-generated fake reviews
(Salminen et al., 2022).

14
Data Collection
Thesis
IMDB Movie
Review Dataset
Well-established benchmark
dataset of movie of IMDB.

Masked Language Modeling (MLM)

Next Sentence Prediction (NPS)
Binary Classification Task

Classification
Token
Separation
Token

For learning more
complex patterns
within Text
Helps to Analyze
context

Fined
Tuned
Bert
Model
• Load pre-trained (BERT
Base Uncased)
• Freeze Transformer
Encoder
• Compile Model

32
Methodology
Pre-
Trained
Model
Google Bert Base
Uncased
Output
Layer
• Softmax activation
• Freezing Encoding
Layers
Training
Process
• Loss function
• Optimizer
Evaluation
• Validation set
Fined
Tuned
Bert
Model

33
Accuracy Comparison
Our tool's performance is compared against existing methods to assess its effectiveness in
AI Content Detection In Binary Approach
80%
74%
76%
72%
67%
59%
GPT-2 Output
Detector
Go Winston
Zero GPT
Verifeye
Compilatio
Turnitin

34
Evolution of Machine Learning Performance
over Time
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12
20k
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Literature Review
Existing Methods
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Rule-Based
Detection
Machine Learning
Techniques

Importance of Detecting
AI-Generated Reviews
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From Classification to Recommendation
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The Machine Learning Toolbox
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Beyond the
Basics
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Unsupervised Learning
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The Art of Feature Engineering
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Evaluating Machine Learning Models
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Beyond Accuracy
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Model Optimization
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Machine Learning in Action
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The Ethics of Machine Learning
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The Future of
Machine Learning
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Deep Learning
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Convolutional Neural Networks
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Reinforcement Learning
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Generative Models
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Machine Learning for Good
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The AI Landscape
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Machine Learning Accuracy vs Complexity
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Global Impact of Machine Learning
Across Industries
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Evolution of Machine Learning Performance
over Time
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Superhuman Skills
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Thank You

Machine Learning - Presentation Template.pptx

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