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Computer Science
Artificial Intelligence
Deep Learning
Deepfakes and Fake News Detection
1. Introduction to Digital Disinformation
2. Foundations of Machine Learning for Media Analysis
3. Core Technologies for Synthetic Media Generation
4. Fake News Detection: Text-Based Analysis
5. Deepfake Detection: Image and Video Analysis
6. Deepfake Detection: Audio Analysis
7. The Adversarial Arms Race
8. Datasets, Evaluation, and Benchmarking
9. Societal, Ethical, and Legal Dimensions
10. Future Trends and Research Frontiers
Datasets, Evaluation, and Benchmarking
Fake News Detection Datasets
Text-Based Datasets
LIAR Dataset
Structure and Annotation Scheme
Fact-Checking Labels
Metadata and Context
Evaluation Protocols
FakeNewsNet
Social Media Integration
Multi-Modal Content
Temporal Information
Network Structure Data
FEVER Dataset
Claim-Evidence Pairs
Wikipedia-Based Verification
Benchmark Tasks and Metrics
Shared Task Competitions
COVID-19 Misinformation Datasets
Pandemic-Specific Content
Health Misinformation Patterns
Multilingual Coverage
Real-Time Collection
Social Media Datasets
Twitter Misinformation Collections
Facebook Fact-Checking Data
Reddit Discussion Analysis
Cross-Platform Propagation Studies
Deepfake Detection Datasets
Video Deepfake Datasets
FaceForensics++
Real vs. Synthetic Video Pairs
Multiple Manipulation Methods
Quality Levels and Compression
Evaluation Benchmarks
Deepfake Detection Challenge Dataset
Large-Scale Collection
Diverse Demographics
Professional Production Quality
Competition Framework
Celeb-DF Dataset
Celebrity-Based Content
High-Quality Synthesis
Challenging Detection Scenarios
Temporal Consistency Focus
DeeperForensics Dataset
Controlled Generation Process
Quality Assessment
Robustness Evaluation
Benchmark Protocols
Audio Deepfake Datasets
ASVspoof Datasets
Spoofing Attack Scenarios
Synthesis and Conversion
Evaluation Protocols
Annual Challenges
Voice Conversion Datasets
Speaker Identity Transfer
Emotional Conversion
Cross-Lingual Synthesis
Quality Assessment
Image Manipulation Datasets
CASIA and Columbia Datasets
NIST Media Forensics Challenges
Synthetic Face Generation Collections
Style Transfer and GANs Datasets
Evaluation Metrics and Methodologies
Classification Performance Metrics
Accuracy and Error Rates
Overall Accuracy
Class-Specific Accuracy
Balanced Accuracy
Top-k Accuracy
Precision and Recall
Positive Predictive Value
Sensitivity Analysis
Class-Specific Metrics
Macro and Micro Averaging
F1-Score and Variants
Harmonic Mean Calculation
Weighted F1-Score
Class-Balanced F1
Multi-Class Extensions
Threshold-Based Metrics
Area Under the Curve
ROC Curve Analysis
Precision-Recall Curves
Multi-Class ROC
Curve Interpretation
Equal Error Rate
False Positive vs. False Negative Trade-off
Operating Point Selection
Threshold Optimization
Cross-Validation Considerations
Specialized Evaluation Approaches
Robustness Testing
Adversarial Evaluation
Transformation Robustness
Cross-Dataset Generalization
Stress Testing Protocols
Temporal Evaluation
Video-Level Metrics
Frame-Level Analysis
Temporal Consistency Scoring
Dynamic Threshold Adaptation
Human Evaluation Studies
Perceptual Quality Assessment
Detection Difficulty Rating
Comparative Studies
User Study Design
Benchmarking and Competition Frameworks
Standardized Evaluation Protocols
Train-Validation-Test Splits
Cross-Validation Strategies
Stratified Sampling
Temporal Split Considerations
Competition Platforms and Challenges
Academic Competitions
Industry Challenges
Open Source Benchmarks
Leaderboard Systems
Reproducibility and Fair Comparison
Code and Model Sharing
Standardized Implementations
Hardware and Software Specifications
Statistical Significance Testing
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9. Societal, Ethical, and Legal Dimensions