An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

多媒体音频设备驱动程序的结构和原理

本文以波形音频输入输出为样本，剖析了Ｗｉｎｄｏｗｓ音频设备驱动程序的结构和原理。Ｗｉｎｄｏｗｓ设备驱动程序为应用提供了可扩展，设备无关，一致的接口。本文给出了数字波形数据Ｉ／Ｏ的处理过程，以及应用程序，驱动程序和音频硬件的层次关系。     

基于小波基优化和动态比特分配的数字音频压缩编码方案

提出了一种新颖的基于自适应小波基优化选择和心理声学模型相结合的数字音频信号的透明质量编码方法，保证固定失真水平上使每帧信号的变换系数的动态分配的比特数最少，并且利用动态码本的方法来消除音频信号的统计冗余，进一步压缩比特率，对于抽样率为４４．１ｋＨｚ每样值用１６比特线性码表示的光盘单声道音乐信号可以压缩到６４ｋＢＰＳ左右。     

一种数字解码卫星电视接收机的原理分析

分析了一种数字解码卫星电视接收机，该机能接收利用ＭＰＥＧ－２进行数据压缩，采用ＳＣＰＣ或ＭＣＰＣ方式上星的卫星电视信号，具有选单显示，断电现场保护等功能。并对该机进行了原理分析，对主要集成电路的基本原理也作了简介，并给出了软件的部分主要流程及功能分析。     

MPEG-2解码器中视频、音频同步的实现

本文分析了理想的MPEG－2信源解码器中视频、音频同步实现的关键，给出了实际的解码器中实现视频、音频同步会遇到的问题和解决的方法。实践证明，这些方法是有效的，已用于我们的MPEG-2信源解码器中。     

A multipurpose audio watermarking algorithm with synchronization and encryption

We propose a new multipurpose audio watermarking scheme in which two complementary watermarks are used.For audio copyright protection,the watermark data with copyright information or signature are first encrypted by Arnold transformation.Then the watermark data are inserted in the low frequency largest significant discrete cosine transform(DCT) coefficients to obtain robustness performance.For audio authentication,a chaotic signal is inserted in the high frequency insignificant DCT coefficients to detect tampered regions.Furthermore,the synchronization code is embedded in the audio statistical characteristics to resist desynchronization attacks.Experimental results show that our proposed method can not only obtain satisfactory detection and tampered location,but also achieve imperceptibility and robustness to common signal processing attacks,such as cropping,shifting,and time scale modification(TSM).Comparison results show that our method outperforms some existing methods.     

小波域最小嵌入失真函数设计及其在隐写中的应用

为了提高隐写方案的安全性,提出一种基于最小嵌入失真原理和网格码的图像隐写算法。首先在离散小波域结合人眼视觉特性和整数提升小波变换设计了失真测度函数,主要考虑了亮度、频率和纹理掩蔽因子对载体失真的影响。然后结合网格码设计了隐写算法,将嵌入信息对载体的修改最小化并且集中在人眼不敏感区域。实验结果显示,方案具有良好的视觉不可见性,且能抵抗空域、小波域等隐写分析的攻击,安全容量达到0.4 bits/pixel。     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

A new approach of audio emotion recognition

A new architecture of intelligent audio emotion recognition is proposed in this paper. It fully utilizes both prosodic and spectral features in its design. It has two main paths in parallel and can recognize 6 emotions. Path 1 is designed based on intensive analysis of different prosodic features. Significant prosodic features are identified to differentiate emotions. Path 2 is designed based on research analysis on spectral features. Extraction of Mel-Frequency Cepstral Coefficient (MFCC) feature is then followed by Bi-directional Principle Component Analysis (BDPCA), Linear Discriminant Analysis (LDA) and Radial Basis Function (RBF) neural classification. This path has 3 parallel BDPCA + LDA + RBF sub-paths structure and each handles two emotions. Fusion modules are also proposed for weights assignment and decision making. The performance of the proposed architecture is evaluated on eNTERFACE’05 and RML databases. Simulation results and comparison have revealed good performance of the proposed recognizer.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.