Deep Fake Detection Using CNN

Advancing deep learning has brought great challenges toward the verification of authenticity in deepfake media, especially about security, privacy, and misinformation. This paper aims to provide a dependable method for detecting synthetic media: via a CNN-based approach named ResNet-50 that deals with a large dataset related to real and deepfake content, capturing subtle anomalies in the model. With further preprocessing, facial alignment, and feature extraction, enhanced accuracy is achieved. Empirical evaluations that demonstrate strong performance against manipulation and varied adversarial attacks underline the need for advance detection technology.