Source cell phone verification from speech recordings using sparse representation

Source recording device recognition is an important emerging research field in digital media forensics. The literature has mainly focused on the source recording device identification problem, whereas few studies have focused on the source recording device verification problem. Sparse representation based classification methods have shown promise for many applications. This paper proposes a source cell phone verification scheme based on sparse representation. It can be further divided into three schemes which utilize exemplar dictionary, unsupervised learned dictionary and supervised learned dictionary respectively. Specifically, the discriminative dictionary learned by supervised learning algorithm, which considers the representational and discriminative power simultaneously compared to the unsupervised learning algorithm, is utilized to further improve the performances of verification systems based on sparse representation. Gaussian supervectors (GSVs) based on MFCCs, which have shown to be effective in capturing the intrinsic characteristics of recording devices, are utilized for constructing and learning dictionary. SCUTPHONE, which is a corpus of speech recordings from 15 cell phones, is presented. Evaluation experiments are conducted on three corpora of speech recordings from cell phones and demonstrate the effectiveness of the proposed methods for cell phone verification. In addition, the influences of number of target examples in the exemplar dictionary and size of the unsupervised learned dictionary on source cell phone verification performance are also analyzed.     

Reachable but not receptive: Enhancing smartphone interruptibility prediction by modelling the extent of user engagement with notifications

Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that predictive models can be built with >80% precision for most users. However we note discrepancies in performance across labelling, training, and evaluation methods, creating design considerations for future systems.     

Modeling Computer Forensics Process from Workflow Perspective

Similar with software process, computer forensics process is also a complex process. There have been a lot of modeling technologies for planning, analyzing and documentation of computer forensics activities. Different from software process, the step order of forensics process is very important. Skipping any step or interchanging one step may lead to the illegality of the whole forensics process, so a precise process is critical. In this paper, Petri net is used to model the computer forensics process.     

分布式网络入侵取证追踪系统的设计与研究

本文首先分析了网络取证的概念,探讨了网络取证的基本原则、数据来源,关键技术以及网络取证的一般过程等问题,并给出了一个分布式网络入侵取证追踪系统的设计。     

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts. A promising solution to this problem is a technique called Generative Adversarial Network (GAN). GAN is a technique that can generate synthetic images that has similar statistics as the training set. A variation of GAN called Conditional GAN (CGAN) enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated. This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation. We also propose a pseudo-labelling technique to label the generated images with proper age and gender. We used the combination of real and generated images to train Dental Age and Sex Net (DASNET), which is a CNN model for dental age estimation. Based on the experiment conducted, the accuracy, coefficient of determination (R2) and Absolute Error (AE) of DASNET have improved to 87%, 0.85 and 1.18 years respectively as opposed to 74%, 0.72 and 3.45 years when DASNET is trained using real, but smaller number of images.     

从64位Windows 7系统物理内存镜像中提取网络连接信息的方法

Memory analysis gains a weight in the area of computer live forensics.How to get network connection information is one of the challenges in memory analysis and plays an important role in identifying sources of malicious cyber attack. It is more difficult to find the drivers and get network connections information from a 64-bit windows 7 memory image file than from a 32-bit operating system memory image file. In this paper, an approach to find drivers and get network connection information from 64-bit windows 7 memory images is given. The method is verified on 64-bit windows 7 version 6.1.7600 and proved reliable and efficient.     

计算机取证的研究现状分析

本文从技术和法律相结合的角度对于国内外计算机取证的研究现状进行了总结和分析。以电子证据的来源为标准,计算机取证技术可分为：单机取证技术、网络取证技术和相关设备取证技术。而以计算机取证的过程为标准,计算机取证技术可以分为：电子证据的发现技术、固定技术、提取技术、分析技术和表达技术等。本文结合这两种分类,全面地分析了计算机取证这一领域的技术法律研究现状,提出了目前存在的问题和解决问题的建议。     

“Digitalizing Walkability”: Comparing Smartphone-Based and Web-Based Approaches to Measuring Neighborhood Walkability in Singapore

We evaluated two digitally enabled approaches to measuring neighborhood walkability: a smartphone-based, on-site pedestrian environmental audit tool, WalkTracker (WTracker), and remote, Web-based (Web) observations. Specifically, we examined street segments and intersections of a neighborhood in Singapore assessing: (1) the Inter-Rater Reliability (IRR) of each approach; (2) the Inter-Method Reliability (IMR) across the two approaches; and (3) the average observation times of the two approaches. Each approach had high IRR for the land use and traffic-related domains, with the Web performing better than WTracker for land use. In these same two domains, the two tools were relatively consistent (high IMR), although higher agreement was found within the tools than across them (IRR higher than IMR). For subjective or fine-grained features, both approaches had low IRR, with the Web-based approach performing worse than the app-based approach. Performance across the instruments was also worse than the reliability of measurements within each instrument (IMR lower than IRR). Some items were not observable via the Web. In terms of observation time, there was no statistically significant time difference in measurements between the two observation methods, not including the round-trip travel time to the site. A hybrid approach, combining the two approaches, might be most appropriate.     

物联网环境下移动终端的发展趋势思考

介绍物联网感知延伸层设备,分析物联网终端市场发展规模以及移动终端的技术发展现状,总结物联网各种业务环境下对移动类物联网终端的需求,提出移动终端针对物联网需求的解决方案,并列举了目前几个典型的移动终端物联网化的应用案例,最后对物联网环境下移动终端的发展趋势进行展望,分析移动终端实现标准化、一体化、小型化、智能化、规模化与低成本化成为针对物联网需求的主要趋势。     

基于有限射影几何的细粒度数据完整性检验方法

 细粒度的数据完整性检验方法在实现完整性检验的同时可以对少数的错误对象进行准确和高效的隔离,从而避免因偶然错误或个别篡改造成整体数据失效的灾难性后果.对细粒度数据完整性检验问题进行了总结和分类,给出了总体的研究思路.为了提高细粒度数据完整性检验方法的错误指示效率,基于有限射影几何原理构造了一种新的数据完整性指示码.其思想是将有限射影几何空间中点与线的关联关系映射为Hash与数据对象之间的监督关系,实现Hash之间完全的均匀交叉.分析了码的主要性能.分析和实验结果表明该码可以准确指示多个错误并且具有更高的压缩率.