经典音频隐写算法综述

音频隐写技术作为信息隐藏的一个重要分支,近些年来越来越受到学术界的关注。本文对LSB隐写算法、回声隐写算法、扩频隐写算法及压缩域隐写算法等几种经典音频隐写算法进行总结和阐述。     

基于视觉感知模型的大容量视频隐写算法

为了获取较大隐写容量和较好不可感知性,结合人眼视觉感知特性,提出一种视觉感知指导数据嵌入的大容量视频隐写算法.该算法综合了多参数空间对比灵敏度函数、基于DCT(discrete cosine transform)块分类的纹理掩蔽效应及运动注意机制,以确定各DCT系数的失真掩蔽测度,进而控制隐写容量.实验结果表明,该隐写算法具有较大隐写容量,同时较好地保持了含密视频的不可感知性.     

基于小波包与自适应预测器的音频隐写分析方法

提出一种基于小波包与自适应预测器的音频隐写分析方法,主要用于检测加性噪声模型.利用加性噪声对音频高频部分比低频部分影响显著的特点,对音频信号进行小波包分解;然后利用最小均方(LMS)自适应预测器对高频小波包系数进行预测,选取预测误差的统计量作为统计特征;最后采用支持向量机分类.实验证明,对于常用的加性噪声隐写方法,即使在嵌入强度或嵌入率较低的情况下,也能达到较高的分类准确率.     

基于码表分布特征和重编码的MP3Stego隐写分析

提出一种基于Huffman码表分布特征和重编码的MP3Stego隐写分析方法.该方法通过Huffman码表分布特征有效地反映了MP3Stego隐写造成的块间量化不均现象.利用重编码得到校准的载体音频,减少不同载体造成的差异,从而提高检测率.实验证明,该方法对MP3Stego有很好的检测效果,在极低的嵌入率下效果仍稳定.     

基于局部相关的图像隐写方法研究

提出了一种新的图像隐写方法。利用载体图像和秘密图像之间的局部相关性,将秘密图像嵌入到载体图像当中。实验结果表明,该方法能够嵌入较多信息和保持良好的载密图像质量,并且可从载密图像中完全提取出秘密信息。     

基于特征函数和质量因子的JPEG图像隐写分析

提出了一种新的JPEG通用隐写分析方法.通过分析JPEG图像DCT域和小波域的量化噪声模型和隐写模型,发现了量化噪声和嵌入噪声对图像的作用原理,并用直方图特征函数来区分2种噪声对图像的影响. 基于JPEG质量因子对图像进行分类,为每个图像分类单独训练支持向量机分类器.实验显示,本文方法相比已有的一些常用JPEG通用隐写分析方法具有更好的检测性能.     

一种直方图失真小的JPEG图像隐写方法

给出了一种JPEG图像的隐写方法,将部分零系数用于矩阵编码来负载秘密信息,不仅提高了信息嵌入容量,还有效减小了直方图失真。将每个8＊8小块中,除直流系数以外的前31个量化系数用于隐藏秘密信息。通过对80幅256＊256灰度图像进行实验,结果表明对于一般的自然图像该算法可以有效地控制直方图失真。     

音频隐写分析技术研究综述

结合实际工作,谈谈音频隐写分析技术研究。     

一种改进的基于马尔科夫链的扩频图像隐写分析方法

提出一种对基于扩频信息隐藏图像进行隐写分析的方法.应用马尔科夫链模型,根据图像的相邻像素点之间的相关性,结合阈值判别方法和改进的模式识别方法,判断一幅图像中是否存在隐写信息.在Corel图像库上的实验表明,本方法降低了虚警率,且正检率的性能优于已有方法.     

基于二值图像的信息隐藏与分析研究

图像信息隐藏是近些年快速发展起来的一种新型信息安全技术,本文在对已有的二值图像隐写方法比较分析基础上,设计了一种通过二值原始图像与隐写图像和隐写图像与二次隐写图像的游程特性变化差值不同,分类确定相应阈值来检测隐秘信息的统计分析方法。实验结果证明这种方法能够成地检测出分块跳转中心像素和分块非跳转中心像素的信息隐藏,并有很高的检测率,实现了对二值图像隐写的盲检测。     

基于GPU加速的扩频信号捕获方法

针对扩频信号捕获中计算量大和运算速度慢的问题,提出基于图形处理器(GPU)加速的捕获方法,将基于循环相关的捕获算法转化为计算统一设备架构(CUDA)线程块执行过程,使扩频捕获过程完全在GPU中加速执行,在保持原有扩频信号捕获概率的同时,显著提高了算法的运算速度.实验结果表明,基于GPU的捕获方法有效地提高了系统的执行效率.     

Homomorphic image watermarking with a singular value decomposition algorithm

In this paper, a new homomorphic image watermarking method implementing the Singular Value Decomposition (SVD) algorithm is presented. The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform. The reflectance component contains most of the image features but with low energy, and hence watermarks embedded in this component will be invisible. A block-by-block implementation of the proposed method is also introduced. The watermark embedding on a block-by-block basis makes the watermark more robust to attacks. A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks. The proposed method is more robust to various attacks. The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security.     

Sentence modeling via multiple word embeddings and multi-level comparison for semantic textual similarity

Recently, using a pretrained word embedding to represent words achieves success in many natural language processing tasks. According to objective functions, different word embedding models capture different aspects of linguistic properties. However, the Semantic Textual Similarity task, which evaluates similarity/relation between two sentences, requires to take into account of these linguistic aspects. Therefore, this research aims to encode various characteristics from multiple sets of word embeddings into one embedding and then learn similarity/relation between sentences via this novel embedding. Representing each word by multiple word embeddings, the proposed MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension.     

Mutual-reinforcement document summarization using embedded graph based sentence clustering for storytelling

In this paper, a document summarization framework for storytelling is proposed to extract essential sentences from a document by exploiting the mutual effects between terms, sentences and clusters. There are three phrases in the framework: document modeling, sentence clustering and sentence ranking. The story document is modeled by a weighted graph with vertexes that represent sentences of the document. The sentences are clustered into different groups to find the latent topics in the story. To alleviate the influence of unrelated sentences in clustering, an embedding process is employed to optimize the document model. The sentences are then ranked according to the mutual effect between terms, sentence as well as clusters, and high-ranked sentences are selected to comprise the summarization of the document. The experimental results on the Document Understanding Conference (DUC) data sets demonstrate the effectiveness of the proposed method in document summarization. The results also show that the embedding process for sentence clustering render the system more robust with respect to different cluster numbers.     

嵌入还是卷入:众包个体缘何贡献知识?

网络和媒体两类社交媒体应用对提升众包个体知识贡献意愿发挥着不同功能,本文基于嵌入理论和卷入理论,探讨众包个体嵌入、卷入与其知识贡献意愿之间的关系。通过结构方程模型和最优尺度回归方法对389份来自我国众包虚拟社区的有效数据进行分析,结果表明:众包个体的网络嵌入和媒体嵌入都对其知识贡献意愿有正向影响,网络嵌入通过个体卷入(企业卷入和社群卷入)对知识贡献意愿产生正向影响,而媒体嵌入对知识贡献意愿的影响存在直接效应和间接效应,既可以直接影响,也可以通过个体卷入间接发挥效应。     

Hotness prediction of scientific topics based on a bibliographic knowledge graph

As a part of innovation in forecasting, scientific topic hotness prediction plays an essential role in dynamic scientific topic assessment and domain knowledge transformation modeling. To improve the topic hotness prediction performance, we propose an innovative model to estimate the co-evolution of scientific topic and bibliographic entities, which leverages a novel dynamic Bibliographic Knowledge Graph (BKG). Then, one can predict the topic hotness by using various kinds of topological entity information, i.e., TopicRank, PaperRank, AuthorRank, and VenueRank, along with pre-trained node embedding, i.e., node2vec embedding, and different pooling techniques. To validate the proposed method, we constructed a new BKG by using 4.5 million PubMed Central publications plus MeSH (Medical Subject Heading) thesaurus and witnessed the essential prediction improvement with extensive experiment outcomes over 10 years observations.     

一种基于整数小波变换的彩色图像盲水印技术

数字水印技术通过在媒体图像中隐藏信号来达到保护版权的作用.本文利用二维离散整数小波变换的快速性,结合人类视觉系统特性,提出了一种基于彩色图像新的、简单的鲁棒性盲水印技术,实现了在彩色图像中嵌入有意义的二值图片.通过分析实验结果,证明了算法满足水印的抗JPEG压缩攻击、对原图的影响较小等特点.     

Dynamic discovery of favorite locations in spatio-temporal social networks

A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users’ social relationships, textual reviews, and POIs’ geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation.     

基于偏移场模型的空变失真图像序列的视频稳定化技术

基于动态偏移场模型,本文提出了一种适用于空变失真图像序列的视频稳定化技术。以空不变图像序列的运动滤波技术为基础,采用空变图像序列的运动估计和频域滤波技术实现了透视失真序列的稳定化。实验结果表明,经稳定化处理后的透视失真视频序列,高频抖动被滤除,图像序列呈现出稳定的透视失真现象。如果已知失真的物理机理,实现稳定化的同时还可以实现透视失真的校正。该技术不仅可以应用在视频稳定化系统中,还可以应用到其他空变失真图像序列的失真校正领域。     

Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts

We propose bidirectional imparting or BiImp, a generalized method for aligning embedding dimensions with concepts during the embedding learning phase. While preserving the semantic structure of the embedding space, BiImp makes dimensions interpretable, which has a critical role in deciphering the black-box behavior of word embeddings. BiImp separately utilizes both directions of a vector space dimension: each direction can be assigned to a different concept. This increases the number of concepts that can be represented in the embedding space. Our experimental results demonstrate the interpretability of BiImp embeddings without making compromises on the semantic task performance. We also use BiImp to reduce gender bias in word embeddings by encoding gender-opposite concepts (e.g., male–female) in a single embedding dimension. These results highlight the potential of BiImp in reducing biases and stereotypes present in word embeddings. Furthermore, task or domain-specific interpretable word embeddings can be obtained by adjusting the corresponding word groups in embedding dimensions according to task or domain. As a result, BiImp offers wide liberty in studying word embeddings without any further effort.