Four-scanning attack on hierarchical digital watermarking method for image tamper detection and recovery

In a recent paper presented by Lin et al., a block-based hierarchical watermarking algorithm for digital images is proposed. It adopts parity check and the intensity-relation check to conduct the experiment of image tamper detection. Their experimental results indicate that the precision of tamper detection and localization is 99.6% and 100% after level-2 and level-3 inspections, respectively. The proposed attacks demonstrate that this watermarking algorithm is fundamentally flawed in that the attacker can tamper a watermarked image easily without being detected. In this paper, a four-scanning attack aimed to Lin et al.'s watermarking method is presented to create tampered images. Furthermore, in case they use encryption to protect their 3-tuple-watermark, we proposed a blind attack to tamper watermarked images without being detected. Experimental results are given to support and enhance our conclusions, and demonstrate that our attacks are successful in tampering watermarked images.     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

基于时间序列法的风电场风速预测研究

针对时间序列的超短期风速预测进行研究,采用Box.Jenkins时间序列分析方法,通过Maflab软件,在达坂城风电场30 m处.每10 min采集一次得到的风速数据,建立ARMA模型,实现提前一小时的风速预测,为更长时间(半天、一天或两天)的风速预测提供理论基础.     

基于LSSVM的木材干燥建模研究

针对木材干燥过程的强非线性特点,提出以最小二乘支持向量机LSSVM建立木材干燥基准模型.通过实验用小型木材干燥窑实际干燥过程中采集的数据作为训练样本进行仿真实验,结果表明基于LSSVM的木材干燥模型预测输出能够准确反映干燥过程木材含水率的变化,模型结构简单、预测精度高、泛化能力强,验证了LSSVM对木材干燥过程建模是一种可行而有效的方法.     

一种基于模糊提取的虹膜鉴别方案

模糊提取从生物特征输入中以容错的方式可靠地提取出均匀分布的随机密钥,当输入发生变化且变化很小时,该密钥可以保持不变,研究了当汉明距离作为生物特征匹配的度量标准时,结合纠错编码学与传统密码学实现模糊提取的虹膜鉴别方法,分析了虹膜特征编码之间的差异对正确鉴别性能的影响,设计了重复码和Reed-Solomon码的两层级联纠错编码方案,并对128只虹膜的各3个样本进行了模拟实验,所给方案使用户虹膜特征模板的安全性和隐私性得到了有效保护,并能够支持用户虹膜的注册更新,此外,实验表明该方案同时具有较理想的识别性能.     

MB-OFDM UWB通信系统维特比解码器的实现

提出一种应用于多波段正交频分复用(MB-OFDM)超宽带通信系统的维特比解码器的设计方案,分析MB-OFDM所采用的卷积/凿孔码及相应的维特比解码算法的性能。为了达到系统要求的最高数据传输率、保持硬件开销的经济性,结合滑动窗口和折叠2种方法设计解码器的硬件结构。在低速工作模式下,部分处理单元被禁用,以节省功耗。该设计经Xilinx Virtex-4 FPGA验证,最高译码速率可达432 Mb/s。     

基于多分类支持向量机的隐写域盲检测

提出一种区分隐写域(包括像素域、DCT域、DWT域)的盲检测方法,构造图像特征向量,建立一个多分类的支持向量机,根据特征向量对图像进行训练。该方法能够识别隐藏信息和其隐写域。实验结果表明,当嵌入容量达到20%时,识别率提高到85%以上。     

Spring Acegi的安全应用与扩展

Spring框架作为一个优秀的多层J2EE系统框架,本身没有提供对系统的安全性支持。Acegi是基于Spring基于IoC（Inversion of Control）和AOP（Aspect Oriented Programming）机制实现的一个安全框架。主要说明了如何在Spring Acegi框架下对资源进行认证和授权,进一步通过对Acegi的扩展,实现了直接从数据库读取权限,增强控制资源的灵活性。