Intelligence for Deepfake Detection: An Experimental Measure to Combat Rising Cyber Threat

Abstract

Deepfake detection technology primarily combines artificial intelligence and machine learning, including deep learning techniques such as Multi-layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and EfficientNet etc. to detect deep fake videos or images using this model. An overview of deepfake detection systems is given in this abstract, along with a focus on important methods, difficulties, and potential future developments. There are several methods for detecting deep fakes, such as analyzing facial features and artifacts using images, analyzing voice characteristics and lip-syncing using audio, and utilizing hybrid approaches that combine several modalities. Difficulties in detecting deep fakes include the quick development of methods for generating deep fakes, the scarcity of labeled training data, and the susceptibility of detection systems to hostile attacks. Despite these obstacles, continuous research endeavors seek to augment the efficiency and expandability of deepfake detection systems via developments in digital forensics, machine learning, and interdisciplinary cooperation. In the future, deepfake detection research will focus on creating multimodal detection systems, integrating blockchain technology for tamper-proof authentication, and investigating explainable AI methods for analyzing detection data.