[Project Addition] : Fake and Real Face Detection using DL

[aaradhyasinghgaur]

Pull Request for DL-Simplified 💡

Issue Title : Fake and Real Face Detection using DL

• Info about the related issue (Aim of the project) : To identify fake and real faces which present a harder challenge to classify them correctly even for the human eye.
• Name: Aaradhya Singh
• GitHub ID: https://github.com/kyra-09
• Email ID: aaradhyasinghgaur@gmail.com
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC-2024 Contributor

Closes: #725

Describe the add-ons or changes you've made 📃

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.
3. 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.) Comaprioson using performance matrices such as accuracy scores , confusion matrix etc.

Type of change ☑️

What sort of change have you made:

• [ ] Bug fix (non-breaking change which fixes an issue)
• [x] New feature (non-breaking change which adds functionality)
• [ ] Code style update (formatting, local variables)
• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [ ] This change requires a documentation update

How Has This Been Tested? ⚙️

Describe how it has been tested Describe how have you verified the changes made

Checklist: ☑️

• [x] My code follows the guidelines of this project.
• [x] I have performed a self-review of my own code.
• [x] I have commented my code, particularly wherever it was hard to understand.
• [x] I have made corresponding changes to the documentation.
• [ ] My changes generate no new warnings.
• [x] I have added things that prove my fix is effective or that my feature works.
• [ ] Any dependent changes have been merged and published in downstream modules.

[github-actions[bot]]

Our team will soon review your PR. Thanks @kyra-09 :)

[abhisheks008]

Hi @kyra-09 please update the project folder name as I have updated the issue name. Please do the needful.

[aaradhyasinghgaur]

okay.. i'll do it right now.

[aaradhyasinghgaur]

@abhisheks008 kindly review my pr . Thank you for your time and work . You have been most helpful and consistent with assigning and reviewing our PRs .