Is your feature request related to a problem? Please describe.
To identify fake and real faces which present a harder challenge to classify them correctly even for the human eye.
Describe the solution you'd like
Data Preparation: Collect and preprocess a balanced dataset of real and fake face images, including normalization, resizing, and augmentation.
Base Model Selection: EfficientNetB0,VGG16 , Xception , InceptionV3 like 5 different models excluding its top layers, to leverage its learned features.
Model Construction: Add custom layers on top of the base model for binary classification, compiling with appropriate loss and metrics.
Initial Training: Train the model with the base layers frozen to only update the new layers.
Fine-Tuning: Unfreeze some or all of the base model layers and continue training with a lower learning rate to fine-tune the entire network.
6.) EDA analysis.
7.) Comparison using performance matrices such as accuracy scores , confusion matrix etc.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional)
A clear and concise description of the approach to be followed.
Additional context
Add any other context or screenshots about the feature request here.
Is your feature request related to a problem? Please describe. To identify fake and real faces which present a harder challenge to classify them correctly even for the human eye.
Describe the solution you'd like
Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional) A clear and concise description of the approach to be followed.
Additional context Add any other context or screenshots about the feature request here.
dataset I'll use :- https://www.kaggle.com/datasets/hamzaboulahia/hardfakevsrealfaces
@akshitagupta15june can you assign this issue to me please?