Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296; Sensors 2018, 18(4), 129

Data curation standards and social science occupational information resources

Occupational information resources - data about the characteristics of different occupational positions - are widely used in the social sciences, across a range of disciplines and international contexts. They are available in many formats, most often constituting small electronic files that are made freely downloadable from academic web-pages. However there are several challenges associated with how occupational information resources are distributed to, and exploited by, social researchers. In this paper we describe features of occupational information resources, and indicate the role digital curation can play in exploiting them. We report upon the strategies used in the GEODE research project (Grid Enabled Occupational Data Environment, http://www.geode.stir.ac.uk). This project attempts to develop long-term standards for the distribution of occupational information resources, by providing a standardized framework-based electronic depository for occupational information resources, and by providing a data indexing service, based on e-Science middleware, which collates occupational information resources and makes them readily accessible to non-specialist social scientists

Spoken affect classification : algorithms and experimental implementation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

Machine-based emotional intelligence is a requirement for natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have gone unexplored in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods

Questions related to Bitcoin and other Informational Money

A collection of questions about Bitcoin and its hypothetical relatives Bitguilder and Bitpenny is formulated. These questions concern technical issues about protocols, security issues, issues about the formalizations of informational monies in various contexts, and issues about forms of use and misuse. Some questions are formulated in the more general setting of informational monies and near-monies. We also formulate questions about legal, psychological, and ethical aspects of informational money. Finally we formulate a number of questions concerning the economical merits of and outlooks for Bitcoin.Comment: 31 pages. In v2 the section on patterns for use and misuse has been improved and expanded with so-called contaminations. Other small improvements were made and 13 additional references have been include