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LR Content Map: Deepfake Detection Using Deep Learning - Coggle Diagram
LR Content Map: Deepfake Detection Using Deep Learning
1- Model Architectures and Approaches
CNN-Based Models
Description: Used to extract spatial features from individual frames.
Observations: Effective for frame-level detection; high accuracy but less effective for temporal inconsistencies.
References: [1], [3], [4], [7], [14]
CNN + LSTM (Hybrid)
Observations: Stronger in video-based detection. Better captures temporal inconsistencies like unnatural blinking or expressions.
[6], [11], [13]
Description: Combines spatial and temporal analysis by adding sequence modeling.
Capsule Networks
Observation: Offers interpretability but is computationally more expensive.
References: [2]
Description: Preserve spatial relationships between features; good for manipulating detection.
Transformer Models
Observatoon: Achieve high performance, but require large datasets and extensive training.
References: [10]
Description: Employ attention mechanisms to model temporal and spatial dependencies.
XGBoost (with CNN features)
Observation: Boosts performance in structured data but may lack interpretability.
References: [15]
Description: Combines deep learning with ensemble learning for classification.
2- Detection Techniques
Spatial-Based Detection
Best for detecting texture and pixel inconsistencies, but weak on temporal artifacts.
References: [1], [3], [4], [7], [14]
Analyzes frame-level features to spot manipulation.
Temporal-Based Detection
Detects subtle shifts and movement inconsistencies; higher complexity and training time.
References: [6], [8], [11], [13]
Focuses on motion patterns, temporal artifacts, and frame relationships.
Biological Signal-Based
Tracks cues like eye blinking, heartbeat, and facial movement.
Effective in controlled settings; sensitive to noise and lighting.
References: [5], [9]
Frequency-Based & Transformer-Based
Effective in capturing subtle manipulations; generalizability is a concern.
References: [10], [12], [15]
Leverages Fourier analysis or attention mechanisms to detect manipulation in frequency or hierarchical space.
4-Dataset Utilization
FaceForensics++
Widely used benchmark; allows model comparison.
References: [1], [3], [4], [6], [11], [14]
Celeb-DF
References: [3], [4], [7]
More challenging; closer to real-world data.
DFDC (DeepFake Detection Challenge)
Very large and diverse; preferred for generalization.
References: [10], [13], [15]
5-Performance and Evaluation Metrics
Accuracy, Precision, Recall
General performance evaluation.
References: All
AUC-ROC
References: [3], [4], [11]
Better suited for imbalanced datasets.
F1-Score
Balances precision and recall.
References: [6], [10], [13]
6-Limitations
Generalization Issues
Models perform poorly on unseen datasets.
References: [3], [7], [10]
High Computational Cost
Especially for CNN-LSTM and transformer-based models.
References: [6], [10], [13]
Data Bias
Datasets lack diversity in ethnicity, lighting, and context.
References: [1], [4], [9]
Explainability
Deep learning models are often black-box systems.
References: [2], [5], [15]
3- Feature Engineering and Input Representation
Handcrafted Features
Includes biological and landmark-based cues.
References: [2], [5], [9]
Optical Flow
Captures motion between frames.
References: [13]
Learned Features
Automatically extracted via CNN/Transformer models.
References: [1], [3], [4], [10]
Landmark Features
Detect facial geometry distortions.
References: [9]