Скачать или смотреть BUILD AN AI DEEPFAKE DETECTOR USING PYTORCH AND OPENCV

In this powerful AI project, you'll learn how to build a deepfake detection system using PyTorch, EfficientNet, and OpenCV. This video is perfect for beginners and intermediate developers who want to understand how artificial intelligence can spot fake content in images and videos.

This system uses a pre-trained EfficientNet B0 model from torchvision to classify images as real or fake based on learned features. It includes data preprocessing, tensor conversion, and prediction without training from scratch — all handled using PyTorch.

What this video covers:

Installing dependencies from requirements.txt

Loading EfficientNetB0 pretrained model from torchvision.models

Transforming input images using torchvision.transforms

Detecting deepfakes by analyzing frame-level predictions

Using OpenCV for image/video input

Running inference with torch.no_grad() for efficiency

Writing clean, modular code in model.py and app.py


Tech stack:

Python 3.11

PyTorch (for inference)

Torchvision (for pretrained models and transforms)

OpenCV (for video/image handling)

NumPy (for array operations)

VS Code as the IDE


Applications of this project:

Detect fake videos in media or news

Build backend for facial verification in security systems

Enhance content moderation tools for social media

Use it in journalism or education to detect misinformation


Who is this for?

AI and ML beginners

Python programmers looking to apply deep learning

Cybersecurity students

Data science learners

Content creators exploring AI ethics

YouTubers doing voiceover tutorials or AI demos


Bonus ideas to expand this:

Add real-time video detection

Integrate with Flask or FastAPI for a web interface

Use Grad-CAM to explain model predictions

Create a dataset of real vs deepfake images


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