A deepfake is AI-generated or manipulated media designed to deceive by convincingly mimicking real people or objects. It differs from ordinary image generation through its deceptive use and risks to trust, privacy, and security. section 13.0 section 13.3
A practical deepfake-detection workflow proceeds through these stages:
- Prepare examples of real and AI-generated images, using a large-scale dataset for fine-tuning. section 13.0
- Start with a pretrained ResNet50 rather than training a new visual model from scratch. section 13.3
- Replace and retrain the final layers so the model classifies images as real or fake. section 13.3
- Test the fine-tuned classifier on unseen images and inspect its classification workflow. section 13.0 section 13.3
Detecting generated images with ResNet50
A pretrained ResNet50 can be adapted to distinguish real images from AI-generated deepfakes. Fine-tuning only the final layers saves time and computational resources while preserving general visual features learned previously. section 13.3
Deepfakes remain difficult to manage because generated images can become nearly indistinguishable from photographs. Their misuse can support misinformation, propaganda, and manipulation, while detection, moderation, responsible-use guidelines, and collaboration remain necessary safeguards. section 13.0 section 13.2
!pip install kagglehub
Install kagglehub in a Jupyter Notebook before preparing the dataset and running the ResNet50 detection workflow. section 13.3
