Implementation of Attention Mechanism in GAN for the Purpose of Quality Detection
Publication Name
Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024
Abstract
The attention mechanism is primarily responsible for improving Generative Adversarial Networks' (GANs) false picture detection. Synthetic images produced by GANs are very lifelike, which has transformed the picture creation area. That same potential, meanwhile, has also sparked worries about the creation of false or misleading material. The attention mechanism facilitates selective emphasis on particular areas of an image during creation. This mechanism is based on human visual perception. It facilitates the discriminator network in GANs to concentrate more on critical regions, which are often hard for traditional GANs to replicate effectively, such as fine structures, texture details, and object consistency. By including attention processes, GANs can detect minor changes between real and synthetic images, significantly improving their ability to identify synthetic information. In order to improve fake picture recognition, this study explores the crucial role that attention processes play in GANs. It also offers insight into the usage, functionality, and potential future research paths of these systems to combat the dissemination of misleading visual information.
Open Access Status
This publication is not available as open access
First Page
1081
Last Page
1087