Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.
Mobile training is an evolution of electronic training and is based on mobile learning technology, which is used to design mobile learning courses. Due to the widespread deployment of mobile devices and the need to remain current with developments in mobile technology, it is important to consider the design of appropriate mobile training content to increase learners' engagement in mobile learning courses. However, studies have emphasized the challenges in this area. Therefore, we conducted a systematic mapping study that offers an overview of the current literature in this domain based on a thorough search of the literature by using a process of selection that involves criteria for inclusion and exclusion, data extraction and synthesis strategies. Of the 194 journal articles identified in the initial search stage, 58 were selected as primary studies; they were published between 2009 and 2019. We applied a classification scheme to answer our research questions. Our study examines the current challenges in the design of mobile training content, identifies the key open issues, determines the trends in publication and emphasizes the most widely researched topics in recent years related to the design of mobile training content. Our study identifies the existing challenges and suggests further work on key open issues. Our study also suggests that, considering the major issues related to pedagogical challenges, the research focus should shift toward the design of attractive, interactive and motivating mobile content that is based on a theoretical framework for mobile training courses, and other technological and managerial challenges that can be addressed should be investigated in order to overcome the existing difficulties in the design of mobile training content and to provide better solutions for the continuity of this research domain.
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