Feature request : Fake-Vs-Real-Face Detection

[aaradhyasinghgaur]

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

1. Data Preparation: Collect and preprocess a balanced dataset of real and fake face images, including normalization, resizing, and augmentation.
2. Base Model Selection: EfficientNetB0,VGG16 , Xception , InceptionV3 like 5 different models excluding its top layers, to leverage its learned features.
3. Model Construction: Add custom layers on top of the base model for binary classification, compiling with appropriate loss and metrics.
4. Initial Training: Train the model with the base layers frozen to only update the new layers.
5. 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)
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Additional context
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dataset I'll use :- https://www.kaggle.com/datasets/hamzaboulahia/hardfakevsrealfaces

@akshitagupta15june can you assign this issue to me please?

[aaradhyasinghgaur]

@akshitagupta15june ma'am if possible kindly assign this issue to me

[aaradhyasinghgaur]

@akshitagupta15june kindly assign this issue to me.

[aaradhyasinghgaur]

@akshitagupta15june can this issue be assigned to me ?