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 steg...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.展开更多
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...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.展开更多
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) ma...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.展开更多
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...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.展开更多
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...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.展开更多
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...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。展开更多
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242in part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘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.
基金Project(2006C11200) supported by the Science and Technology Project of Zhejiang Province of China
文摘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.
基金supported by the NSFC(61173141,61362032,U1536206, 61232016,U1405254,61373133,61502242,61572258)BK20150925+4 种基金the Natural Science Foundation of Jiangxi Province, China(20151BAB207003)the Fund of Jiangsu Engineering Center of Network Monitoring(KJR1402)the Fund of MOE Internet Innovation Platform(KJRP1403)the CICAEET fundthe PAPD fund
文摘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.
基金This work was funded by the Deanship of Scientific Research at Jouf University under grant No DSR-2021-02-0398.
文摘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.
基金Supported by the Foundation of Anhui Province Key Laboratory of Physical Geographic Environment(2022PGE010)The Fundamental Research Funds for the Central Universities,CHD(300102353508)+5 种基金the Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application,MNR(22101)National Natural Science Foundation of China(61801211)Natural Science Foundation of Jiangsu Province(BK20221478)Hong Kong Scholars Program(XJ2022043)S&T Program of Hebei(21567624H)Open Project Program of Key Laboratory of Meteorology and Ecological Environment of Hebei Province(Z202102YH)。
文摘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.
文摘准确、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。