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.
