Project Title : Deepfake Detection

Objective:
To develop a machine learning model that can detect whether a video or image is real or manipulated (deepfake), helping to identify fake content generated using AI.

What are Deepfakes?
Deepfakes are synthetic media—usually videos or images—created using deep learning techniques like GANs (Generative Adversarial Networks) to swap faces or manipulate speech.

Key Concepts:

Detect unnatural artifacts in face movements, lighting, blinking, lip-sync, or texture.

Use computer vision + ML to extract and analyze these features.

Steps Involved:

Dataset Collection:

Use datasets like FaceForensics++, DFDC (Deepfake Detection Challenge), Celeb-DF, etc., containing real and deepfake videos/images.

Preprocessing:

Extract frames from videos.

Detect and crop faces using tools like OpenCV or Dlib.

Resize and normalize images for model input.

Feature Extraction:

Extract spatial features (image patterns).

Optionally use temporal features (changes across video frames).

Model Building:

Use CNNs (Convolutional Neural Networks) for image-based detection.

Advanced: Combine with RNNs or 3D CNNs for video-based detection.

Common architectures: XceptionNet, EfficientNet, ResNet.

Model Evaluation:

Use accuracy, precision, recall, F1-score, and ROC curve.

Validate with separate test sets or cross-validation.

Deployment (Optional):

Create a web app or browser plugin to detect deepfakes in real time.

Applications:

Fake news and misinformation detection

Content verification in media/journalism

Legal evidence validation

Social media content moderation

Tools & Technologies:

Languages: Python

Libraries: TensorFlow/Keras, OpenCV, Dlib, Scikit-learn, FFmpeg