Deepfake Detection Using CNN-Based Architecture

Deepfakes refer to synthetic media generated using artificial intelligence (AI) and deep learning techniques, capable of manipulating or fabricating visual and audio content to produce highly realistic yet deceptive multimedia. The rapid evolution and widespread accessibility of these technologies have raised serious concerns about their potential misuse across various domains, including politics, entertainment, and security, where the implications could be far-reaching and disruptive. This paper delves into the application of convolutional neural network (CNN)-based architectures for effectively detecting deepfakes in images, aiming to provide a robust and reliable solution to mitigate their adverse impacts. Through a detailed investigation of different CNN models, this study evaluates their ability to distinguish between authentic and fabricated visual data by leveraging feature extraction and classification mechanisms. Furthermore, the research explores mathematical formulations central to image analysis, such as feature maps, loss functions, and performance metrics, providing insights into the intricate processes driving accurate deepfake detection. The findings from this study contribute significantly to advancing the field of imagebased AI and offer practical solutions for real-world applications where preserving authenticity is paramount.