# SUMMARY
The project addresses the pressing issue of accurately identifying and categorizing deepfake photos, aiming to mitigate concerns over their potential misuse and the spread of misinformation. As deep fake technology advances, traditional detection methods struggle to differentiate between real and fake content. By exploring diverse machine learning and deep learning models, the research seeks to improve cybersecurity measures against deep fake manipulation threats by enhancing detection accuracy and developing robust systems.
# OBJECTIVES
The project aims to enhance deep fake detection accuracy through advanced architecture exploration, transfer learning, and hyperparameter fine-tuning, empowering models to better discern real from altered content. Additionally, it seeks to develop and deploy an intuitive user interface for easy media content analysis, enabling users to combat misinformation effectively.
# METHODOLOGY
The methodology begins with using CNNs for deep fake detection, followed by exploring advanced architectures like ResNets and DenseNet. GANs generate additional data, while transfer learning with VGG and ResNet is refined. Model parameters are optimized through techniques like grid search and Bayesian optimization, with consideration for ensemble methods. Iterative changes guided by continuous evaluation lead to applying XAI techniques for insights into model decisions.
# EVALUATION
Evaluation will encompass qualitative and quantitative analysis. Qualitatively, a visual inspection of detection results will be conducted alongside a comparison of original and predicted images. Quantitatively, metrics like accuracy, precision, recall, and F1-score will be calculated. Receiver Operating Characteristic (ROC) curves and confusion matrices will aid in model comparison.
# DATASETS:
https://www.kaggle.com/datasets/manjilkarki/deepfake-and-real images/data?select=Dataset https://github.com/ondyari/FaceForensics/tree/master/dataset
# REFERENCES:
https://zenodo.org/records/5528418#.YpdlS2hBzDd https://github.com/ondyari/FaceForensics