Detection Of Deep Fakes Using CNN

Abstract

The rise of technology has emphasized the importance of identifying altered images and videos to preserve trust and authenticity in digital content. Convolutional Neural Networks (CNNs) have emerged as tools, for tasks like detecting fakes. This study introduces a method for identifying fakes using CNNs. Our strategy involves training a CNN model on a dataset containing both synthetic visuals. The network is structured to learn features that differentiate between manipulated content. We use a mix of layers pooling layers and connected layers to extract and process features from the input data. To strengthen the reliability of our model we employ techniques like data augmentation and transfer learning. Data augmentation includes applying transformations such as rotation, scaling and cropping to the training data to enhance its variety. Transfer learning allows us to utilize trained CNN models and adjust them for deep fake detection purposes. We assess the effectiveness of our approach on datasets. Compare it with existing techniques. Our experiments show that our CNN based method achieves accuracy in spotting fake content in different scenarios. Additionally we examine how well the model withstands attacks and variations, in input data quality.