一种基于双随机相位编码的光学加密系统设计

针对于有加密需求的监控领域,图像数据剧增对加密算法提出严峻考验。复杂的加解密算法将会增大运算开支,最终影响视频传输的实时性能。本文根据视频加解密要求,提出在视频监控领域进行光学加解密并设计了基于双随机相位编码的单通道加解密光学系统。该光学系统加解密速度和光速相等,加解密时耗几乎为零,在高清视频迅速普及的未来将会产生重要的应用价值,在军事加解密领域将有着广阔的应用前景。     

一种基于TLM超混沌细胞神经网络图像加密新算法

混沌对初值敏感的特性使得它适合于数据加密。以4 阶CNN 模型为基础,提出了一种新的超混沌细胞神经网络图像加密算法。算法分为置乱和扩散二个阶段,复合混沌映射用于生成置乱阶段控制参数,用以置乱图像行列之间的高度互相关像素。在扩散阶段,使用不同初始状态和参数的复合混沌映射生成高阶混沌细胞神经网络的初始条件,以生成扩散阶段的密钥流。算法的已知明文和选择明文攻击、密钥空间和直方图的仿真实验均取得了良好的结果。与其他相关算法相比,该算法具有密钥敏感性和抗攻击性强的优点,适用于图像加密。     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

基于混沌密钥的视频单通道光学加密技术研究

针对于目前监控视频像素点数量日益增大的现状 ,提出了一种利用光学手段进行视频加密的方法, 将加密速率等同为了光速,彻底解决了图像数据量急剧增大给现行加密算法带来的巨大压力 。利用超混沌系 统对光学加密技术中的双随机相位编码系统进行了改造,并对超混沌序列的产生过程进行了 推导。在提高加 密效率的同时,极大程度地提升了视频传输过程中的安全性。同时,使用彩色图像通道分离 技术实现了单通道 彩色视频图像加密,降低了系统的复杂度,提升了系统的可实现性。对本文所提加密方法的 加 密过程的仿真结果表明,本文提出的加密技术能够获得理想的视频加密效 果。与基于双随机相位编码的光加密技术进行对比的结果表明,本文系统在相邻像素相关性 方面具有更高的抗攻击能力。     

Structure-prior deep neural network for lane detection

Lane detection is an important task of road environment perception for autonomous driving. Deep learning methods based on semantic segmentation have been successfully applied to lane detection, but they require considerable computational cost for high complexity. The lane detection is treated as a particular semantic segmentation task due to the prior structural information of lane markings which have long continuous shape. Most traditional CNN are designed for the representation learning of semantic information, while this prior structural information is not fully exploited. In this paper, we propose a recurrent slice convolution module (called RSCM) to exploit the prior structural information of lane markings. The proposed RSCM is a special recurrent network structure with several slice convolution units (called SCU). The RSCM could obtain stronger semantic representation through the propagation of the prior structural information in SCU. Furthermore, we design a distance loss in consideration of the prior structure of lane markings. The lane detection network can be trained more steadily via the overall loss function formed by combining segmentation loss with the distance loss. The experimental results show the effectiveness of our method. We achieve excellent computation efficiency while keeping decent detection quality on lane detection benchmarks and the computational cost of our method is much lower than the state-of-the-art methods.     

EEG-based prediction of driver's cognitive performance by deep convolutional neural network

We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms.     

Convolutional neural network based deep conditional random fields for stereo matching

Stereo matching has been studied for many years and is still a challenge problem. The Markov Random Fields (MRF) model and the Conditional Random Fields (CRF) model based methods have achieved good performance recently. Based on these pioneer works, a deep conditional random fields based stereo matching algorithm is proposed in this paper, which draws a connection between the Convolutional Neural Network (CNN) and CRF. The object knowledge is used as a soft constraint, which can effectively improve the depth estimation accuracy. Moreover, we proposed a CNN potential function that learns the potentials of CRF in a CNN framework. The inference of the CRF model is formulated as a Recurrent Neural Network (RNN). A variety of experiments have been conducted on KITTI and Middlebury benchmark. The results show that the proposed algorithm can produce state-of-the-art results and outperform other MRF-based or CRF-based methods.     

A new hardware Trojan detection technique using deep convolutional neural network

The involvement of external vendors in semiconductor industries increases the chance of hardware Trojan (HT) insertion in different phases of the integrated circuit (IC) design. Recently, several partial reverse engineering (RE) based HT detection techniques are reported, which attempt to reduce the time and complexity involved in the full RE process by applying machine learning or image processing techniques in IC images. However, these techniques fail to extract the relevant image features, not robust to image variations, complicated, less generalizable, and possess a low detection rate. Therefore, to overcome the above limitations, this paper proposes a new partial RE based HT detection technique that detects Trojans from IC layout images using Deep Convolutional Neural Network (DCNN). The proposed DCNN model consists of stacking several convolutional and pooling layers. It layer-wise extracts and selects the most relevant and robust features automatically from the IC images and eliminates the need to apply the feature extraction algorithm separately. To prevent the over-training of the DCNN model, a new stopping condition method and two new metrics, namely Accuracy difference measure (ADM) and Loss difference measure (LDM), are proposed that halts the training only when the performance of our model genuinely drops. Further, to combat the issue of process variations and fabrication noise generated during the RE process, we include noisy images with varying parameters in the training process of the model. We also apply the data augmentation and regularization techniques in the model to address the issues of underfitting and overfitting. Experimental evaluation shows that the proposed technique provides 99% and 97.4% accuracy on Trust-Hub and synthetic ISCAS dataset, respectively, which is on-an-average 15.83% and 21.69% higher than the existing partial RE based techniques.     

一种基于四维混沌神经网络的通信数据加密算法

混沌神经网络序列具有带宽大、难于预测和重构等特点,因而非常适用于网络通信和数据加密.本文提出一种四维混沌神经网络序列的产生方法,利用四维混沌神经网络进行混合加密,所产生的二值序列对明文进行预处理.实验结果表明,该系统对产生的二值序列,具有良好的初值敏感度,序列随机性较为理想,同时,四维混沌神经网络大大增加了密钥空间,利...     

双生物识别图象的光学加密方法研究

本文提出了一种将两幅生物识别图象通过一套光学系统进行同时加密的方法。该方法先将一幅图象转换为纯相位和进行纯相位编码,再对另一幅图象编码,然后经过4f光学系统作频率域纯相位编码,并利用与编码过程类似的方法进行解码。可以证明该方法的编码图象为恒定的白噪声。解码过程具有鲁棒性,相位部分图象加密的安全性要高于振幅部分图象的加密。并分析了加性高斯噪声对解码图象的影响     

基于计算全息的串联式三随机相位板图像加密

本文提出一种串联式三随机相位板图像加密的新方法,该方法充分运用计算全息记录复值光场的特性以记录加密图像,在传统的双随机相位加密系统基础上,置人第三个随机相位板在输出平面上,对输出的计算全息图进行相位调制加密,引入了新的密钥,获得很好的双密钥效果!同时由于计算全息周再现的多频特性,解密须正确提取单元频谱,进一步提高了图像...     

基于Hopfield神经网络模型的加密解密原理分析

本文介绍一种基于Hopfield神经网络模型的加密解密专用芯片设计方案,采用传统的弱金匙(Weak Key)和半弱金匙(Semi-weak Key)的加密方法会降低安全性,而在本文中所采用的Hopfield神经网络模型却能避免出现此弱点,本文还针对加密解密步骤做了具体的分析,加密和解密安全性和有效性大幅度提升.     

基于深度神经网络的自适应波束形成算法

阵列天线接收到的期望信号和干扰信号,其入射的到达角度(Angle of Arrival,AOA)总是快速变化的,而传统波束形成算法计算量大,无法实时计算。针对这一问题,提出了一种基于深度神经网络的自适应波束形成(Deep Neural Network Adaptive Beamforming,DNNABF)算法,用入射信号AOA组成的向量作为网络输入,网络输出逼近最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)算法求得的权矢量。仿真结果表明,卷积神经网络(Convolutional Neural Network,CNN)与DNNABF方法都能准确拟合MVDR算法权矢量,可在入射信号AOA快速变化时自适应地形成波束和零陷,但DNN计算速度相对MVDR有将近6.5倍的提升,训练模型时间也远低于CNN。     

周期性相位掩模用于图象加密的研究

提出了一种新的行之有效的图象加密方法．即利用两个彼此独立的周期性相位掩模分别对需要保护的图象在空域和Fourier频域进行编码，使原始图象变成噪声。其特点是解密时对相位掩模的对准精度有一定的宽容度．而且由于该相位分布具有周期性，是密钥的合法持有者唯一掌握的确定性函数，所以可以重构，给实际应用带来了便利。     

一种改进的深度神经网络应用于推荐系统

推荐系统是信息过滤的一种重要工具。随着互联网和大数据的介入,推荐系统的技术革新面临着新的挑战。近年来,深度学习的革命性进步在语音识别、图像分析和自然语言处理方面都受到了广泛关注。与此同时,一种应用于许多复杂任务的最先进的机器学习技术被用于推荐系统,以提高推荐的质量。由于其一流的性能表现和高质量的推荐结果,深度学习可以更好地理解用户需求、项目特征及其之间的历史性互动。文章提出将一种改进的深度神经网络应用于推荐系统。实验结果表明,该方法的效果令人瞩目。     

一种图像混沌网络加密法

在信息时代的今天,随着网络技术和多媒体技术的高速发展和广泛应用,越来越多的信息在网络上方便传递,但这同时也带来了信息安全隐患问题.因而,保护信息安全就显得尤为重要.本文利用四维混沌系统产生的二值序列,设定神经网络的权值和阈值,对像素进行加密解密运算.该算法具有计算复杂度大、实现无失真加密、密钥空间大的特点.仿真实验分析...     

Video frame interpolation using deep cascaded network structure

Video frame interpolation is a technology that generates high frame rate videos from low frame rate videos by using the correlation between consecutive frames. Presently, convolutional neural networks (CNN) exhibit outstanding performance in image processing and computer vision. Many variant methods of CNN have been proposed for video frame interpolation by estimating either dense motion flows or kernels for moving objects. However, most methods focus on estimating accurate motion. In this study, we exhaustively analyze the advantages of both motion estimation schemes and propose a cascaded system to maximize the advantages of both the schemes. The proposed cascaded network consists of three autoencoder networks, that process the initial frame interpolation and its refinement. The quantitative and qualitative evaluations demonstrate that the proposed cascaded structure exhibits a promising performance compared to currently existing state-of-the-art-methods.     

随机偏振光学加密算法的加密及解密特性分析

为了阐明随机偏振模板在光学加密系统中的作用,采用理论分析与数值模拟相结合的方法,进行了在双随机偏振光学加密系统中偏振模板的特性参量对系统加密效果及解密误差的影响的理论和仿真分析;对比了随机相位模板和随机偏振模板对光学加密系统加密效果及解密误差的不同影响。结果表明,加密方面,采用随机偏振模板或随机相位模板均能生成平稳白噪声分布的密文,但是偏振模板有两个构造参量,密钥空间更大;解密方面,系统对相位模板以及偏振模板中不同参量的解密敏感性有一定差异,随机偏振加密具有更高的应用灵活性。该结果对理解相位及偏振编码光学加密系统的本质属性及设计安全性更高的光学加密系统有一定的帮助。     

网络传输透明加密系统设计与实现

随着计算机网络的普及和加密技术的发展,透明加密技术已经成为目前企业等单位用于文档数据保密的首选。文件的透明加密技术具有以下特点:不影响使用者的原有操作习惯,自动对需要保护的文件进行加密和解密,并且保存在磁盘上的文件始终是已经过加密的。在网络环境下的文件传输中,利用透明加密技术对文件加以保护也是十分必要的。但是,用于加密和解密的密钥应该仅由已授权的用户保管,通常是使用者个人。因此,对于网络传输的透明加密,如何使密钥在不同的使用者之间完成交换共享成为一个关键的问题。针对这个问题以及透明加密技术的特点,开发一个文件的网络传输透明加密系统,实现了应用于网络传输的文件透明加密等功能。