Deepguardnet: A Resnet-Based Hybrid Framework for Intelligent Deepfake Image and Video Authentication

The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forgery media which become hard to differentiate from authentic media; this leads to misinformation, identity spoofing, and digital scams. Therefore, precise and effective authentication of multimedia becomes an imperative requirement for digital forensics investigation and online content authentication. This research paper presents a ResNet-powered deep feature learning approach for detecting Deepfake images and videos. The suggested approach normalizes and resizes images, while videos are decomposed into frames for thorough spatial-temporal analysis. The hybrid convolutional neural network model, which is built on top of ResNet architecture, extracts discriminative features that represent subtle manipulation traces, face texture inconsistency, and structural abnormalities. In addition, inverted residual blocks and linear bottlenecks are used to increase computational efficiency. The deep learning-based feature extraction process is then followed by the classification stage to distinguish between genuine multimedia content and forged multimedia content. From experimental studies, it can be shown that the proposed framework helps to enhance the detection rate, robustness toward new Deepfake methods, and enables real-time implementation. The research provides an effective solution for multimedia authentication applications.