Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion

22IoT-Integrated Detection and Classification of Deepfake Images and Videos Using Custom Deep Learning Models

K. Praveen Kumar^* and R. Ramprashath

School of Computer Applications, Karpagam College of Engineering, Coimbatore, India

Abstract

Deep fakes are becoming more common; they include editing previously published films and photos to produce content that appears authentic but is wholly fake. The development process has been considerably expedited by the widespread availability of deep learning techniques, such as autoencoders, generative adversarial networks, and user-friendly software. These sophisticated algorithms adeptly fuse and modify visual and audio elements, facilitating the production of content that closely mimics genuine footage, even for those without specialized knowledge. The malicious manipulation of images and videos poses significant security and societal concerns. With an emphasis on facial alteration, the goal of this research is to create a deep learning system integrated with Internet of Things (IoT) for the detection and classification of deepfake images and videos. The dataset used for the project is either FaceForensics++, Celeb-DF, or the Deepfake Detection Challenge Dataset, available on Kaggle, consisting of real and deepfake images and videos. By utilizing recurrent and convolutional neural networks (CNNs), we have made developments in deepfake detection. Commencing with preprocessing the data, extracting frames from the videos, and separating ...