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 this paper,we propose a novel coverless image steganographic scheme based on a generative model.In our scheme,the secret image is first fed to the generative model database,to generate a meaning-normal and independ...In this paper,we propose a novel coverless image steganographic scheme based on a generative model.In our scheme,the secret image is first fed to the generative model database,to generate a meaning-normal and independent image different from the secret image.The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image.Thus,we only need to transmit the meaning-normal image which is not related to the secret image,and we can achieve the same effect as the transmission of the secret image.This is the first time to propose the coverless image information steganographic scheme based on generative model,compared with the traditional image steganography.The transmitted image is not embedded with any information of the secret image in this method,therefore,can effectively resist steganalysis tools.Experimental results show that our scheme has high capacity,security and reliability.展开更多
To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based...To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based on whole feature mapping,which has poor robustness when facing geometric attacks,because the contents in the image are easy to lost.To solve this problem,we use ResNet to extract semantic features,and segment the object areas from the image through Mask RCNN for information hiding.These selected object areas have ethical structural integrity and are not located in the visual center of the image,reducing the information loss of malicious attacks.Then,these object areas will be binarized to generate hash sequences for information mapping.In transmission,only a set of stego-images unrelated to the secret information are transmitted,so it can fundamentally resist steganalysis.At the same time,since both Mask RCNN and ResNet have excellent robustness,pre-training the model through supervised learning can achieve good performance.The robust hash algorithm can also resist attacks during transmission.Although image segmentation will reduce the capacity,multiple object areas can be extracted from an image to ensure the capacity to a certain extent.Experimental results show that compared with other coverless image steganography methods,our method is more robust when facing geometric attacks.展开更多
Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels,creating the stego image.However,the left traces that are caused by these ...Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels,creating the stego image.However,the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload.A coverless data hiding concept is proposed to solve this issue.Coverless does not mean that cover is not required,or the payload can be transmitted without a cover.Instead,the payload is embedded by cover generation or a secret message mapping between the cover file and the payload.In this paper,a new coverless image steganography method has been proposed based on the jigsaw puzzle image generation driven by a secret message.Firstly,the image is divided into equal rows then further divided into equal columns,creating blocks(i.e.,sub-images).Then,according to secret message bits and a proposed mapping function,each block will have tabs/blanks to get the shape of a puzzle piece creating a fully shaped jigsaw puzzle stego-image.After that,the generated jigsaw puzzle image is sent to the receiver.Experimental results and analysis show a good performance in the hiding capacity,security,and robustness compared with existing coverless image steganography methods.展开更多
With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet a...With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography.For a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity comparison.Then the mapping indexes can be constructed based on LBP feature and hash generation.After mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user.The user can extract the privacy information successfully with a similar method of feature analysis and index construction.The simulation results show good performance of robustness.And the hiding success rate also shows good feasibility and practicability for application.Since the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.展开更多
At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity ...At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.展开更多
In the field of information hiding,text is less redundant,which leads to less space to hide information and challenging work for researchers.Based on the Markov chain model,this paper proposes an improved evaluation i...In the field of information hiding,text is less redundant,which leads to less space to hide information and challenging work for researchers.Based on the Markov chain model,this paper proposes an improved evaluation index and onebit embedding coverless text steganography method.In the steganography process,this method did not simply take the transition probability as the optimization basis of the steganography model,but combined it with the sentence length in the corresponding nodes in the model to gauge sentence quality.Based on this,only two optimal conjunctions of the current words are retained in the method to generate sentences of higher quality.Because the size of the training text dataset is generally large,this leads to higher complexity of the steganographic model;hence,fewer repetitions of the generated steganographic sentences occur.Different datasets and methods were selected to test the quality of the model.The results indicate that our method can achieve higher hiding capacity and has better concealment capability.展开更多
Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image an...Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.展开更多
Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data.One issue with these methods is that as the steganographic capacity increases,the number of images st...Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data.One issue with these methods is that as the steganographic capacity increases,the number of images stored in the database grows exponentially.This makes it challenging to build and manage a large image database.To improve the image library utilization and anti-attack capability of the steganography system,we propose an efficient coverless scheme based on dynamically matched substrings.We utilize You Only Look Once(YOLO)for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors.Using this dictionary,each image is effectively assigned to a specific scrambling factor,which is then used to scramble the receiver’s sequence key.To achieve sufficient steganography capability with a limited image library,all substrings of the scrambled sequences have the potential to hide data.After matching the secret information,the ideal number of stego images will be obtained from the database.According to experimental results,this technology outperforms most previous works in terms of data load,transmission security,and hiding capacity.It can recover an average of 79.85%of secret information under typical geometric attacks,and only approximately 200 random images are needed to achieve a capacity of 19 bits per image.展开更多
The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only t...The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.展开更多
The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data ...The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data concealing has been suggested by researchers called coverless steganography.Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image.This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems.The system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the maze.Firstly,a full grid is generated with several specific rows and columns determined from the number of bits of the secret message.Then,these bits are fed to the full grid to form the maze game stego image.Finally,the generated maze game stego image is sent to the recipient.The experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.展开更多
With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep...With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods,however both of which have their limitations.The former is difcult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm.Due to the irreversibility of the process of generating secret images from message codeword,the recovery accuracy of the latter is very poor.To this end,this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography(JoCS).Firstly,this paper proposes the Semantic Factorization Fitting module(SeFF)and the Transform Domain Steganography module(TrDS).The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network,which establishs a mapping rule between message codeword and the coarse feature of the generated image,and then the extractor is used to ft the above mapping rule,which has excellent robustness and completeness;the latter encodes the main content area of the image based on the encoder in VQGAN,and then adds secret message to the latent vector of the encoded image,which achieves the steganography in the latent domain of the image.Secondly,we demonstrate the independence between two modules and the advantages of connecting two modules.By using the image generated in the SeFF module as the cover image in the TrDS module,secondary steganography of a single image is achieved,based on which we design the JoCS scheme.The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100%completeness,excellent image quality and outstanding robustness against various image attacks.Moreover,our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication.Finally,this paper also discusses the following three points as further elaboration of the scheme:(1)the advantages of the mapping rule in the SeFF module(2)the verifcation of the independence between the two modules(3)the fexibility of the joint steganography scheme.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This paper was supported by the National Natural Science Foundation of China(No.U1204606)the Key Programs for Science and Technology Development of Henan Province(No.172102210335)Key Scientific Research Projects in Henan Universities(No.16A520058).
文摘In this paper,we propose a novel coverless image steganographic scheme based on a generative model.In our scheme,the secret image is first fed to the generative model database,to generate a meaning-normal and independent image different from the secret image.The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image.Thus,we only need to transmit the meaning-normal image which is not related to the secret image,and we can achieve the same effect as the transmission of the secret image.This is the first time to propose the coverless image information steganographic scheme based on generative model,compared with the traditional image steganography.The transmitted image is not embedded with any information of the secret image in this method,therefore,can effectively resist steganalysis tools.Experimental results show that our scheme has high capacity,security and reliability.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,http://kjt.hunan.gov.cn/+3 种基金in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/and in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/.
文摘To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based on whole feature mapping,which has poor robustness when facing geometric attacks,because the contents in the image are easy to lost.To solve this problem,we use ResNet to extract semantic features,and segment the object areas from the image through Mask RCNN for information hiding.These selected object areas have ethical structural integrity and are not located in the visual center of the image,reducing the information loss of malicious attacks.Then,these object areas will be binarized to generate hash sequences for information mapping.In transmission,only a set of stego-images unrelated to the secret information are transmitted,so it can fundamentally resist steganalysis.At the same time,since both Mask RCNN and ResNet have excellent robustness,pre-training the model through supervised learning can achieve good performance.The robust hash algorithm can also resist attacks during transmission.Although image segmentation will reduce the capacity,multiple object areas can be extracted from an image to ensure the capacity to a certain extent.Experimental results show that compared with other coverless image steganography methods,our method is more robust when facing geometric attacks.
基金funded by“Taif University Researchers Supporting Project No.(TURSP-2020/160),Taif University,Taif,Saudi Arabia.”。
文摘Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels,creating the stego image.However,the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload.A coverless data hiding concept is proposed to solve this issue.Coverless does not mean that cover is not required,or the payload can be transmitted without a cover.Instead,the payload is embedded by cover generation or a secret message mapping between the cover file and the payload.In this paper,a new coverless image steganography method has been proposed based on the jigsaw puzzle image generation driven by a secret message.Firstly,the image is divided into equal rows then further divided into equal columns,creating blocks(i.e.,sub-images).Then,according to secret message bits and a proposed mapping function,each block will have tabs/blanks to get the shape of a puzzle piece creating a fully shaped jigsaw puzzle stego-image.After that,the generated jigsaw puzzle image is sent to the receiver.Experimental results and analysis show a good performance in the hiding capacity,security,and robustness compared with existing coverless image steganography methods.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,and 2019SK2022,author H.T,http://kjt.hunan.gov.cn/+4 种基金in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,and Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the National Natural Science Foundation of Hunan under Grant 2019JJ50866,author L.T,2020JJ4140,author Y.T,and 2020JJ4141,author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/and in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/.
文摘With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography.For a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity comparison.Then the mapping indexes can be constructed based on LBP feature and hash generation.After mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user.The user can extract the privacy information successfully with a similar method of feature analysis and index construction.The simulation results show good performance of robustness.And the hiding success rate also shows good feasibility and practicability for application.Since the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.
基金the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grant U1836208,U1536206,U1836110,61602253,61672294+2 种基金by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.
基金This work is supported under the National Key Research and Development Program of China(2018YFB1003205)in part under the 2019 Longyuan Youth Innovation and Entrepreneurship Talents(Team)Project(GanZuTongZi[2019]No.39)(No.23).
文摘In the field of information hiding,text is less redundant,which leads to less space to hide information and challenging work for researchers.Based on the Markov chain model,this paper proposes an improved evaluation index and onebit embedding coverless text steganography method.In the steganography process,this method did not simply take the transition probability as the optimization basis of the steganography model,but combined it with the sentence length in the corresponding nodes in the model to gauge sentence quality.Based on this,only two optimal conjunctions of the current words are retained in the method to generate sentences of higher quality.Because the size of the training text dataset is generally large,this leads to higher complexity of the steganographic model;hence,fewer repetitions of the generated steganographic sentences occur.Different datasets and methods were selected to test the quality of the model.The results indicate that our method can achieve higher hiding capacity and has better concealment capability.
基金This work was supported in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133the Natural Science Foundation of Hunan Province under Grant 2020JJ4141,2020JJ4140the National Natural Science Foundation of China under Grant 62002392.
文摘Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.
文摘Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data.One issue with these methods is that as the steganographic capacity increases,the number of images stored in the database grows exponentially.This makes it challenging to build and manage a large image database.To improve the image library utilization and anti-attack capability of the steganography system,we propose an efficient coverless scheme based on dynamically matched substrings.We utilize You Only Look Once(YOLO)for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors.Using this dictionary,each image is effectively assigned to a specific scrambling factor,which is then used to scramble the receiver’s sequence key.To achieve sufficient steganography capability with a limited image library,all substrings of the scrambled sequences have the potential to hide data.After matching the secret information,the ideal number of stego images will be obtained from the database.According to experimental results,this technology outperforms most previous works in terms of data load,transmission security,and hiding capacity.It can recover an average of 79.85%of secret information under typical geometric attacks,and only approximately 200 random images are needed to achieve a capacity of 19 bits per image.
基金This work was supported by the Korea Technology and Information Promotion Agency(TIPA)for SMEs grant funded by the Korea government(Ministry of SMEs and Startups)(No.S3271954)the National Research Foundation of Korea(NRF)grant funded by the korea government(MSIT)(No.2022H1D8A3038040)the Soonchunhyang University Research Fund.
文摘The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.
基金Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia.
文摘The trend of digital information transformation has become a topic of interest.Many data are threatening;thus,protecting such data from attackers is considered an essential process.Recently,a new methodology for data concealing has been suggested by researchers called coverless steganography.Coverless steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an image.This paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional systems.The system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the maze.Firstly,a full grid is generated with several specific rows and columns determined from the number of bits of the secret message.Then,these bits are fed to the full grid to form the maze game stego image.Finally,the generated maze game stego image is sent to the recipient.The experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.
基金supported in part by the National Natural Science Foundation of China under Grant U23B2002in part by the National Natural Science Foundation of China under Grant 62272007.
文摘With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods,however both of which have their limitations.The former is difcult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm.Due to the irreversibility of the process of generating secret images from message codeword,the recovery accuracy of the latter is very poor.To this end,this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography(JoCS).Firstly,this paper proposes the Semantic Factorization Fitting module(SeFF)and the Transform Domain Steganography module(TrDS).The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network,which establishs a mapping rule between message codeword and the coarse feature of the generated image,and then the extractor is used to ft the above mapping rule,which has excellent robustness and completeness;the latter encodes the main content area of the image based on the encoder in VQGAN,and then adds secret message to the latent vector of the encoded image,which achieves the steganography in the latent domain of the image.Secondly,we demonstrate the independence between two modules and the advantages of connecting two modules.By using the image generated in the SeFF module as the cover image in the TrDS module,secondary steganography of a single image is achieved,based on which we design the JoCS scheme.The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100%completeness,excellent image quality and outstanding robustness against various image attacks.Moreover,our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication.Finally,this paper also discusses the following three points as further elaboration of the scheme:(1)the advantages of the mapping rule in the SeFF module(2)the verifcation of the independence between the two modules(3)the fexibility of the joint steganography scheme.
