Deep learning technologies, such as neural networks, have tackled complicated issues from large-scale data processing to computer vision and human-level control. Editing photographs with software designed for commercial purposes makes it easy for anyone to manipulate images in order to create fictitious ones. Generative adversarial networks (GANs) are currently being utilised to generate superficially realistic photographs, as opposed to the old methods that were previously used to create phony images. The GANs images are referred to as deep fakes. To the unaided eye, they appear to be real photographs. As a result, it's impossible to spot a phony image produced with GANs. The social network is made less safe as a result of the uploading of these bogus photographs. It is therefore critical that the digital image's legitimacy be detected before it is uploaded. Thus, in this chapter, the authors suggest a few pre-trained deep learning frameworks that may be used to effectively detect deep fake images.
Apples are the most productive fruits in the world with a lot of medicinal and nutritional value. Significant economic losses occur frequently due to various diseases that occur on a huge scale of apple production. Consequently, the effective and timely discovery of apple leaf infection becomes compulsory. The proposed work uses optimal deep neural network for effectively identifying the diseases of apple trees. This work utilizes a convolution neural network to capture the features of Apple leaves. Extracted features are optimized with the help of the optimization algorithm. The optimized features are utilized in the leaf disease identification process. Here the traditional DNN algorithm is modified by means of weight optimization using adaptive monarch butterfly optimization (AMBO) algorithm. The experimental results show that the proposed disease identification methodology based on the optimized deep neural network accomplishes an overall accuracy of 98.42%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.