Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

Network-aware perceptual error concealment method for H.264 video with side information

In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.

大亚湾近岸浅海马尾藻遥感信息提取

马尾藻对健康的沿海生态系统具有重要作用。遥感监测多针对开阔海域的漂浮马尾藻,对浅海底栖型马尾藻研究较少;技术方法大多基于经验模型,依赖大量实测数据。本文基于水体辐射传输过程,建立马尾藻遥感信息提取模型。大亚湾3个试验结果表明:①马尾藻丰度分布情况与野外调查数据基本吻合;②大亚湾的马尾藻在水域开阔程度高、水体交换条件良好、受一定风浪影响、水深小于1.6 m的环境条件下,长势更好,丰度更高;③水深、海浪作用是影响马尾藻生长分布的重要环境因子。本文方法对马尾藻资源的保护和海岸带生物多样性建设具有积极意义。

基于注意力机制和图像轮廓的实例分割算法

基于图像轮廓的实例分割方法利用少量轮廓顶点来表示物体,减少了算法的参数量,提高了算法的运行效率,但导致算法的精度低于传统逐像素处理的分割算法,获得的分割结果质量较差。为提升算法的准确性,文中提出一种基于图像轮廓结合注意力机制的实例分割模型(Attend the Contour snake,AC-snake)。在主干网络中加入改进的大卷积核(Largekernel+)提升模型的感受野,提取更加丰富的特征信息。改进轮廓顶点变形阶段的网络结构,结合双通道注意力模块(Dual Channel attention,DC-attentio)加强轮廓顶点的有效信息,减少训练网络中的无效参数,提升检测精度和训练速度。实验结果表明,在Cityscapes验证数据集中,相较于原始模型,文中提出的改进模型性能有所提升。

基于全卷积神经网络的无人机巡检图像边缘检测方法

由于无人机巡检图像边缘检测的距离误差大、图像清晰度低,提出基于全卷积神经网络的无人机巡检图像边缘检测方法。采用水平集量化特征分解方法,提取无人机巡检所采集图像的多尺度边缘特征;采用全卷积神经网络构建图像边缘检测模型结构,优化损失函数,完成模型的局部和整体训练,并将多尺度边缘特征输入深度学习网络;采用二阶导数计算像素边缘概率,检测图像的弱边缘并生成边缘信息概率图,计算无人机巡检图像弱边缘对象的概率值,实现图像边缘细化。实验结果表明,所提方法能有效获取图像中目标对象的边缘特征,距离误差均小于0.25,图像清晰度均在24以上,能够完整、可靠获取图像中不同位置、物体等目标的边缘结果,且边缘检测结果更为精细。

基于遥感图像的不动产地理信息采集方法研究

现有的不动产地理信息采集方法难以精准地划分不动产边界,导致不动产地理信息采集结果误差较大,为此,提出基于遥感图像的不动产地理信息采集方法。以角二阶矩参量和相关性为基础,提取遥感图像的特征参量,根据其划分采集范围内各个不动产的边界信息,通过匹配目标不动产像素在遥感图像空间维度网格范围内位置,实现对其地理信息的采集。测试结果表明,设计方法的Kappa系数均值为0.687,图像信息采集精度均在97%以上,不动产地理信息采集结果与实际信息基本一致,未出现缺失情况,因此说明所提出的地理信息采集方法可以降低采集误差。

Steganalysis of LSB Matching Using Characteristic Function Moment of Pixel Differences

Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

基于梯度分块的直方图平移可逆信息隐藏

针对大多数基于直方图平移的可逆信息隐藏中载体图像分块方法过于粗糙的问题,根据相邻像素之间具有相似特性,提出对载体图像按像素梯度进行分割,将图像分为平滑区域和粗糙区域。同时为减少像素的无效平移,只对平滑区域分块,并采用直方图的次峰值点进行双向移位实现信息可逆隐藏,同时将零值点嵌入图像;不需要单独传送峰值点和零值点信息,降低了通信负担。实验结果表明:所提方法在图像质量和峰值信噪比上具有明显的提升,可应用于保密传输等领域。

从全局到局部:双注意力融合去雾网络

为了处理现有的基于卷积神经网络去雾方法只使用单一的注意力、很难生成细节生动的清晰图像,且容易导致色彩失真的问题,提出了一个全局与局部注意力融合的图像去雾方法,以获得正常清晰度和无色彩失真的去雾图像。首先利用通道注意力将输入的有雾图像在通道维度切分为两部分,一部分送入通道像素注意力通道抽取局部特征,另一部分送入Transformer通道学习全局特征,然后利用像素注意力对两个通道学习的特征进行融合,将上述模块作为基本单元组合为一个多级U型去雾网络,增加残差连接缓解上下采样导致的细节信息丢失,最后在网络底层加入一个Transformer模块学习全局信息。在多个公开可用的去雾图像数据集RESIDE SOTS Indoor、RESIDE SOTS Outdoor上测试所提方法的有效性,结果表明:对比经典的去雾方法,所提网络生成的图像细节更丰富并且色彩失真最少;在RESIDE SOTS Outdoor数据集上,相比经典的FFA-Net,峰值信噪比提高1.16 dB,相比GridDehazeNet,峰值信噪比提高3.68 dB。提出的全局与局部注意力融合方法能有效地去除雾霾,提升图像的对比度与清晰度,设计的多级U型去雾网络和残差连接结构能够缓解细节丢失,提升去雾效果,获得清晰的图像。

混合像元分解在石漠化信息提取中的应用进展

为有效实现石漠化信息提取,概括性地从像元级和亚像元级2方面介绍石漠化信息的遥感提取方法,重点介绍混合像元分解技术的数据源、端元提取方法、模型算法及结果应用,结果表明,混合像元分解技术能较好地解决混合像元问题,为利用非线性混合像元分解模型开展石漠化信息提取提供参考。

一种融合局部像素信息和改进NLFCM的SAR图像分割方法

针对传统局部信息模糊C均值聚类算法权重系数仅由像素间欧式距离决定,无法准确衡量和充分利用像素间的相似性,对SAR图像分割不准确的问题,提出了一种全新的局部信息相似性描述方法,并结合图像的非局部信息,对像素到聚类中心的距离和像素隶属度计算方法进行改进,并提出了一种同时包含图像局部和非局部信息的改进SAR图像分割方法。实验表明,与其他模糊聚类方法相比,该方法在抑制SAR图像相干斑噪声的同时,能较好地保护SAR图像目标的边缘和细节,具有很好的SAR图像分割效果。

基于像素差分卷积神经网络的类岩石裂缝检测方法

针对现有裂缝检测算法提取的局部梯度信息不足而导致裂缝识别精度低的问题,提出了一种基于像素差分的编码器-解码器U型结构的裂缝检测方法。使用由像素差分卷积块和残差块组成的PDCBlock作为网络的编码器,将两种不同方向上的像素邻域的差分值计算融合到卷积网络运算中,以充分获取裂缝语义信息的同时更好地捕获局部梯度信息,使得在背景复杂的情景下裂缝边缘识别更准确。在混凝土类岩石裂缝数据集CRACK500上对比了同类方法,实验结果表明:该方法在上述数据集上的平均交并比和Dsc分别为0.436 3、0.580 4,裂缝分割精度、相似性上均优于对比方法。

基于不规则尺度区域光谱信息的高光谱图像亚像元定位

亚像元定位技术可以分析混合像元,并实现从丰度图像到亚像元级精细土地覆盖定位图像的转换。然而,传统的亚像元定位方法所使用的光谱信息通常在指定的矩形局部窗口中构造,并且很少使用所有波段的光谱信息,影响了亚像元定位的性能。为了解决这一问题,本文提出了一种基于不规则尺度区域光谱信息的高光谱图像亚像元定位方法(SIISA)。在三幅遥感图像上的实验结果表明,所提出的SIISA优于现有的亚像元定位方法。

基于后验信息的压缩感知图像重构方法

单像素相机的测量数据可采用正交匹配追踪(orthogonal matchingpursuit,OMP)算法重构,但是重构得到的图像质量不好。本文提出一种提高图像重构效果的方法,该方法利用先验信息(列模平均值)判断重构效果优劣;通过测量数据相邻列相减的操作,以增强稀疏度。首先,基于OMP对测量数据逐列重构;然后,对重构效果差的列,作相邻列相减的操作后逐列重构;最后,通过逐列累加操作得到重构效果更好的图像。实验结果表明,该方法在仅使用OMP的情况下可以使重构图像的信噪比(signal tonoiseratio,SNR)得到很大提高,虽然重构图像上有很明显的条纹,但是重构结果依然获得很大改善。

PIE系列遥感软件在测绘类专业体验式教学中的应用

遥感数据处理软件作为遥感数据与行业应用的桥梁,在我国遥感产业化发展过程中发挥着不可替代的作用。为了从源头上减轻对国外遥感数据处理软件的依赖,满足日益增长的国家经济和安全需要,有必要开展国产遥感软件在测绘类专业实践教学中的应用研究,促进国产遥感软件在高校测绘类专业的使用和发展。本文从测绘类本科专业遥感实践课程的教学需要出发,分析了像素专家(PIE)系列国产遥感数据处理软件在高校教学中应用的必要性和适用性,充分利用体验式教学理念和翻转课堂,设计了相关教学案例并在本科教学实践中进行试验,为PIE在测绘类专业体验式实践教学中的推广应用提供了重要借鉴。同时,对推动国产遥感软件在实践教学中的发展提供了重要参考。

Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

The intact data transmission to the authentic user is becoming crucial at every moment in the current era.Steganography;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of data.The resilience and imperceptibility are improved by choosing an appropriate embedding position.This paper gives a novel system to immerse the secret information in different videos with different methods.An audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB Technique.The Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD technology.Stego-audio is linked again with the stego-video in the third stage.The incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research work.The spread spectrum approach is used to increase the complexity of secret data recognition.Two different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

信息融合背景下的网络安全态势感知模型及关键技术分析

本文基于信息融合构建的网络安全态势感知模型,可通过态势信息的采集、预处理、信息融合与态势评估,超前识别入侵事件,并对入侵事件的发展态势作出准确预测,以便于网络管理员提前采取预防措施,达到防患于未然的效果,维护网络资产设备的安全。在网络安全态势感知与评估中,主要应用到了Hyperic-Sigar态势信息收集技术、NSSAP数据交换技术、决策级数据融合技术以及基于矩阵法的网络安全态势评估技术等关键技术。

基于PIE-Engine融合改进特征的农作物分类研究

准确、及时地了解作物种植结构和信息在粮食安全、经济到政治等人类活动的许多方面都发挥着至关重要的作用。基于结合面向对象随机森林算法(Random forest,RF)和一站式地球科学大数据实时计算平台(Pixel Information Expert-Engine),探讨了结合面向对象随机森林算法与时间序列哨兵1号合成孔径雷达(SAR)数据后向散射系数对大规模作物分类的影响,并结合哨兵1号和哨兵2号主被动遥感数据,探讨植被指数特征和纹理特征的不同组合对后向散射系数、光谱特征和作物分类精度的提高。结果表明,结合面向对象随机森林算法,可明显削弱分类的椒盐效果,且基于融合时间序列的多特征SAR和光学数据的分类精度最高,SAR数据的分类精度最低。本研究采用的方法和平台能够准确、高效地进行土地利用分类工作,具有很好的推广价值。

像元信息分解和决策树相结合的影像分类方法

该文提出了一种基于像元信息分解和决策树相结合的遥感自动分类方法。选择广州市番禺区作为研究区,用像元信息分解和多变量决策树法把TM影像分为水体、植被、水泥地、土壤4种基本组分,分离成4类树枝;分别以4种基本地物组分作为分类掩膜,采用BP神经网络分类、形状指数提取、光谱特征提取等复合方法进行分枝,并开展野外遥感调查,以提高和验证分类精度。结果表明:该方法保证了分枝时地物的纯洁度,有效地避免了地物提取时多余信息的干扰和影响,提高了分类精度。结合实地调查数据与最大似然分类算法进行对比实验,表明该模型比最大似然总体分类精度高16%。