Deepfake detection using Convolution Neural Network

The rapid growth of synthetic media has made Deepfake detection a critical challenge in digital forensics, social media security, and information integrity. This work presents an effective Deepfake detection framework based on a Convolutional Neural Network (CNN) implemented using MATLAB. The proposed system detects manipulated images and videos by learning discriminative facial features that distinguish real content from Deepfake content. Initially, frames are extracted from both real and Deepfake videos, while real facial images are also collected. All samples are organized into separate real and fake categories, labeled, and divided into training, validation, and testing sets. Data augmentation techniques are applied to enhance generalization and reduce overfitting. A CNN model is then designed and trained to automatically learn spatial patterns related to facial inconsistencies, texture artifacts, and boundary distortions commonly introduced during Deepfake generation. After training, the model is evaluated on unseen data to measure detection accuracy and robustness. The system further allows users to upload an image or video for testing, providing a classification result along with a confidence score. To improve transparency and trust, visual explanation techniques are incorporated in the form of heatmaps, which highlight facial regions such as eyes, lips, and contours that influenced the model’s decision. These explanations help users understand why a sample is classified as real or fake. Experimental validation using a publicly available Kaggle Deepfake dataset demonstrates the effectiveness of the proposed CNN-based approach.

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