Fine-grained cooperative access control scheme with hidden policies

The traditional ciphertext policy attribute-based encryption (CP-ABE) has two problems:one is that the access policy must be embedded in the ciphertext and sent, which leads to the disclosure of user爷 s privacy information, the other is that it does not support collaborative decryption, which cannot meet the actual demand of conditional collaborative decryption among multiple users. In order to deal with the above two problems at the same time, a fine-grained cooperative access control scheme with hidden policies (FCAC-HP) is proposed based on the existing CP-ABE schemes combined with blockchain technology. In FCAC-HP scheme, users are grouped by group identifier so that only users within the same group can cooperate. In the data encryption stage, the access policy is encrypted and then embedded in the ciphertext to protect the privacy information of the access policy. In the data access stage, the anonymous attribute matching technology is introduced so that only matched users can decrypt ciphertext data to improve the efficiency of the system. In this process, a smart contract is used to execute the verification algorithm to ensure the credibility of the results. In terms of security, FCAC-HP scheme is based on the prime subgroup discriminative assumption and is proved to be indistinguishable under chosen plaintext attack (CPA) by dual system encryption technology. Experimental verification and analysis show that FCAC-HP scheme improves computational efficiency while implementing complex functions.     

融合UWB与INS的消防员室内定位与NLOS检测算法

融合超宽带（UWB）和惯性导航系统（INS）能够实现消防员室内精确定位。为实现UWB的非视距（NLOS）误差检测,设计一种双级EKF框架。该框架以松耦合形式实现UWB/INS的数据融合,通过INS获取的初始位置估计坐标以检测UWB测量值的NLOS误差,根据检测结果计算残差矩阵来动态调整融合滤波器的测量噪声矩阵,以达到缓解NLOS误差的目的。实验结果表明,与三角不等式原理检测算法和无NLOS检测的UWB/INS简单融合算法相比,所提NLOS检测算法具备良好的检测能力、较强的稳定性及较高的定位精度。     

面向深度学习的对抗样本差异性检测方法

深度神经网络（DNN）在许多深度学习关键系统如人脸识别、智能驾驶中被证明容易受到对抗样本攻击,而对多种类对抗样本的检测还存在着检测不充分以及检测效率低的问题,为此,提出一种面向深度学习模型的对抗样本差异性检测方法。首先,构建工业化生产中常用的残差神经网络模型作为对抗样本生成与检测系统的模型;然后,利用多种对抗攻击攻击深度学习模型以产生对抗样本组;最终,构建样本差异性检测系统,包含置信度检测、感知度检测及抗干扰度检测三个子检测系统共7项检测方法。在MNIST与Cifar-10数据集上的实验结果表明,属于不同对抗攻击的对抗样本在置信度、感知度、抗干扰度等各项性能检测上存在明显差异,如感知度各项指标优异的对抗样本在置信度以及抗干扰度的检测中,相较于其他类的对抗样本表现出明显不足;同时,证明了在两个数据集上呈现出差异的一致性。通过运用该检测方法,能有效提升模型对对抗样本检测的全面性与多样性。     

基于AHP—TOPSIS的渗透测试工具的综合评价模型

综合TOPSIS和AHP方法,针对网络安全工具难以精确抉择的问题,提出一种简单、易实现且灵活性较强的综合评价模型。首先是从各个工具的功能、使用平台、使用人员期望以及用户评分等4个评判指标进行展开分析,建立出综合评价模型的指标体系;其次,利用AHP法和Matlab软件确定各指标相应的客观权重并进行综合分析并建立判断矩阵;再次,使用TOPSIS法分析计算最优解的逼近度,同时确定最符合用户需求的工具。最后,结合指标体系和需求选取部分工具进行实验验证。证明了此模型的可行性和灵活性,同时结果表明,该模型对于网络研究人员在抉择工具时有一定的参考价值。同时该模型也可适用于其他工具的选择。     

结合边缘信息和门卷积的人脸修复算法

针对任意形状遮挡下人脸修复,现有方法容易产生边缘模糊和恢复结果失真等问题。提出了一种结合边缘信息和门卷积的人脸修复算法。首先,通过先验人脸知识产生遮挡区域的边缘图,以约束人脸修复过程。其次,利用门卷积在部分像素缺失下的精确局部特征描述能力,设计面向图像修复的门卷积深度生成对抗网络(GAN)。该模型由边缘连接生成对抗网络和图像修复生成对抗网络两部分组成。边缘连接网络利用二值遮挡图和待修复图像及其边缘图的多源信息进行训练,实现对缺失边缘图像的自动补全和连接。图像修复网络以补全的边缘图为引导信息,联合遮挡图像进行缺失区域修复。实验结果表明:相比其他算法,该算法修复效果更好,其评价指标比当前基于深度学习的图像修复算法更优。     

K均值聚类和支持向量数据描述的图像超分辨率算法

目的 为了提高图像超分辨率算法对数据奇异点的鲁棒性,提出一种采用K均值聚类和支持向量数据描述的图像超分辨率重建算法(Kmeans-SVDD)。方法 训练过程:首先用K均值聚类算法将训练图像的近似子带划分为若干类,然后用支持向量数据描述去除每类数据的奇异点,最后在小波域内用主成分分析训练近似子带和细节子带字典。测试过程:根据同一场景高低分辨率图像近似子带相似这一现象,首先将待重建低分辨率测试图像的近似子带作为相应高分辨率测试图像的近似子带,然后由训练得到的字典恢复出高分辨率测试图像的细节子带,最后通过逆小波变换得到高分辨率测试图像。结果 相比于当前双三次插值、Zeyde、ANR与Kmeans-PCA算法,Kmeans-SVDD算法重建的高分辨率测试图像的平均峰值信噪比依次提高了1.82 dB、0.37 dB、0.30 dB、0.15 dB。结论 通过大量实验发现,在字典训练之前加入SVDD过程可以去除离群点,提高字典质量。在小波域中将各频带分开重建,可避免低频图像中包含的不可靠高频信息对超分辨率结果的影响,从而恢复出可靠的高频信息。     

使用Hash表和树位图的两级IPv6地址查找算法

为了提高IPv6地址查找效率,在分析IPv6路由前缀长度分布规律的基础上,提出了基于哈希表及树位图(Tree-bitmap)的两级IPv6地址查找算法.算法将长度为16,32,48和64比特的前缀分别存储在4个Hash表中,其余前缀的前16,32和48比特利用已有的Hash表存储,剩余的不足16比特的部分前缀利用树位图存储,并将树位图的入口地址保存在Hash表中.IP地址查找时在Hash表和树位图中进行两级查找.实验表明,该查找算法的平均内存访问次数为1～2,最坏情况下为7,适用于高速IPv6地址查找.     

两个新的数字图像加密效果评价准则

图像信息熵和灰度变化平均值是两个常用的图像加密效果评价准则。分析指出,图像信息熵和灰度变化平均值受图像尺寸大小的影响较大,为此提出了两个新的图像加密效果评价准则:直方图均衡度和游程统计量。直方图均衡度可用于基于像素灰度值变换的加密过程的评价,游程统计量可用于图像坐标置换的加密过程的评价。新方法表达简单,计算便捷,其显著优点是与图像大小无关,不需要借助原图像。     

Plasmon-Assisted Self-Encrypted All-Optical Memory

All-optical responsive nanomaterials, which can rapidly switch between two stable states, have been regarded as the next-generation memories due to their potential to realize binary information storage and implement on-chip, integrated photonic neuromorphic systems. Rare earth oxides are preeminent candidates owing to their extraordinary luminescent stability and narrow optical transitions. However, due to the lack of simple and effective optical switches, it is difficult to realize all-optical data storage, encoding, and retrieval by pure rare earth-doped luminescent nanoparticles. Here, a rapid and high-contrast of 10⁴ luminescent switching of Y₂O₃:Eu³⁺ nanoparticle between the enhancement and quenching states is achieved by employing the strong light confinement and ultrafast thermal response of localized surface plasmon resonance. A self-encrypted all-optical memory is presented with optical information writing, encryption, reading, and re-writing, and a high-sensitivity synaptic response of emitters to frequency and light intensity flux, which can be harnessed to encrypt information flows and promote convenient and high-security information encryption. Such a convenient and secure plasmonic thermally assisted self-encrypting luminescent switch paves the way for constructing high-performance stimuli-responsive rare earth oxide crystals on demand and expanding their applications in various data encryption, anti-counterfeiting, and rewritable colouration devices.