Guan-Zhong Wu, Xiangyu Yu, Hui-hua Liang, Minting Li
Recently, convolutional neural network has been introduced to information hiding and deep net- work has shown great potential in steganography. However, one drawback of deep network is that it’s sensitive to small fluctuations. In previous works, the encoder-decoder structure is trained end-to-end, but in practice, encoder and decoder should be used separately. Therefore, end-to-end trained steganography networks are vulnerable to fluctuations and the secret decoded from those networks suffers from unpleasant noise. In this work, we present an image-in-image steganog- raphy method called TISGAN to achieve better results, both in image quality and security. In particular, we divide the training process into two parts. Moreover, perceptual loss is applied to encoder, to improve security in our work. We also append a denoising structure to the end of de- coder to achieve better image quality. Finally, the adversarial structure with useful techniques employed is also used in secret revealed process.
{"title":"Two-Step Image-in-Image Steganography via GAN","authors":"Guan-Zhong Wu, Xiangyu Yu, Hui-hua Liang, Minting Li","doi":"10.4018/ijdcf.295814","DOIUrl":"https://doi.org/10.4018/ijdcf.295814","url":null,"abstract":"Recently, convolutional neural network has been introduced to information hiding and deep net- work has shown great potential in steganography. However, one drawback of deep network is that it’s sensitive to small fluctuations. In previous works, the encoder-decoder structure is trained end-to-end, but in practice, encoder and decoder should be used separately. Therefore, end-to-end trained steganography networks are vulnerable to fluctuations and the secret decoded from those networks suffers from unpleasant noise. In this work, we present an image-in-image steganog- raphy method called TISGAN to achieve better results, both in image quality and security. In particular, we divide the training process into two parts. Moreover, perceptual loss is applied to encoder, to improve security in our work. We also append a denoising structure to the end of de- coder to achieve better image quality. Finally, the adversarial structure with useful techniques employed is also used in secret revealed process.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"179 2 1","pages":"1-12"},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77639729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.4018/ijdcf.20211101.oa11
Yong Liu, Dawen Xu
Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.
{"title":"HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding","authors":"Yong Liu, Dawen Xu","doi":"10.4018/ijdcf.20211101.oa11","DOIUrl":"https://doi.org/10.4018/ijdcf.20211101.oa11","url":null,"abstract":"Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"22 1","pages":"1-15"},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90073092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.4018/IJDCF.20211101.OA8
Shane Miller, K. Curran, T. Lunney
The detection of unauthorised users can be problematic for techniques that are available at present if the nefarious actors are using identity hiding tools such as anonymising proxies or virtual private networks (VPNs). This work presents computational models to address the limitations currently experienced in detecting VPN traffic. The experiments conducted to classify OpenVPN usage found that the neural network was able to correctly identify the VPN traffic with an overall accuracy of 93.71%. These results demonstrate a significant advancement in the detection of unauthorised user access with evidence showing that there could be further advances for research in this field particularly in the application of business security where the detection of VPN usage is important to an organization.
{"title":"Identifying the Use of Anonymising Proxies to Conceal Source IP Addresses","authors":"Shane Miller, K. Curran, T. Lunney","doi":"10.4018/IJDCF.20211101.OA8","DOIUrl":"https://doi.org/10.4018/IJDCF.20211101.OA8","url":null,"abstract":"The detection of unauthorised users can be problematic for techniques that are available at present if the nefarious actors are using identity hiding tools such as anonymising proxies or virtual private networks (VPNs). This work presents computational models to address the limitations currently experienced in detecting VPN traffic. The experiments conducted to classify OpenVPN usage found that the neural network was able to correctly identify the VPN traffic with an overall accuracy of 93.71%. These results demonstrate a significant advancement in the detection of unauthorised user access with evidence showing that there could be further advances for research in this field particularly in the application of business security where the detection of VPN usage is important to an organization.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"27 1","pages":"1-20"},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88261248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a compressive sensing(CS)-based scheme that combines encryption and data hiding to provide double protection to the image data in the cloud outsourcing. Different domain techniques are integrated for efficiency and security. After the data holder gets the sample of the raw data, he embeds watermark into sample and encrypts it, and then sends the protected sample to cloud for storage and recovery. Cloud cannot get any information about either the original data or watermark in the CS recovery process. Finally, users can extract the watermark and decrypt the data recovered by cloud directly in sparse domain. At the same time, after extracting the watermark, the image data of user will be closer to the original data compared with the data without extraction. Besides, the counter (CTR) mode is introduced to generate the measurement matrix of CS, which can improve security while avoiding the storage of measurement matrixes. The experimental results demonstrate that the scheme can provide both privacy protection and copyright protection with high efficiency.
{"title":"Cloud-Assisted Image Double Protection System With Encryption and Data Hiding Based on Compressive Sensing","authors":"Di Xiao, Jia Liang, Y. Xiang, Jiaqi Zhou","doi":"10.4018/ijdcf.295812","DOIUrl":"https://doi.org/10.4018/ijdcf.295812","url":null,"abstract":"In this paper, we propose a compressive sensing(CS)-based scheme that combines encryption and data hiding to provide double protection to the image data in the cloud outsourcing. Different domain techniques are integrated for efficiency and security. After the data holder gets the sample of the raw data, he embeds watermark into sample and encrypts it, and then sends the protected sample to cloud for storage and recovery. Cloud cannot get any information about either the original data or watermark in the CS recovery process. Finally, users can extract the watermark and decrypt the data recovered by cloud directly in sparse domain. At the same time, after extracting the watermark, the image data of user will be closer to the original data compared with the data without extraction. Besides, the counter (CTR) mode is introduced to generate the measurement matrix of CS, which can improve security while avoiding the storage of measurement matrixes. The experimental results demonstrate that the scheme can provide both privacy protection and copyright protection with high efficiency.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"111 1","pages":"1-19"},"PeriodicalIF":0.7,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73953824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA8
R. Sunitha, J. Chandrika
The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.
{"title":"Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks","authors":"R. Sunitha, J. Chandrika","doi":"10.4018/IJDCF.20210901.OA8","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA8","url":null,"abstract":"The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"15 1","pages":"130-144"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82496891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA7
M. S. Das, A. Govardhan, D. Vijayalakshmi
The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.
{"title":"A Model of Cloud Forensic Application With Assurance of Cloud Log","authors":"M. S. Das, A. Govardhan, D. Vijayalakshmi","doi":"10.4018/IJDCF.20210901.OA7","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA7","url":null,"abstract":"The key concepts of digital forensic investigation in cloud computing are examination and investigation. Cybercriminals target cloud-based web applications due to presence of vulnerabilities. Forensic investigation is a complex process, where a set of activities are involved. The cloud log history plays an important role in the investigation and evidence collection. The existing model in cloud log information requires more security. The proposed model used for forensic application with the assurance of cloud log that helps the digital and cloud forensic investigators for collecting forensic scientific evidences. The cloud preservation and cloud log data encryption method is implemented in java. The real-time dataset, network dataset results tell that attacks with the highest attack type are generic type, and a case conducted chat log will predict the attacks in advance by keywork antology learning process, NLP, and AI techniques.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"1 1","pages":"114-129"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89529590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA2
Barkha Shree, Parneeta Dhaliwal
Recent developments in digital forensics (DF) have emphasized that along with inspection of digital evidence, the study of behavioural clues based on behavioural evidence analysis (BEA) is vital for accurate and complete criminal investigation. This paper reviews the existing BEA approaches and process models and concludes the lack of standardisation in the BEA process. The research comprehends that existing BEA methodologies are restricted to specific characteristics of the forensic domain in question. To address these limitations, the paper proposes a standardised approach detailing the step-by-step implementation of BEA in the DF process. The proposed model presents a homogenous technique that can be practically applied to real-life cases. This standard BEA framework classifies digital evidence into categories to decipher associated offender characteristics. Unlike existing models, this new approach collects evidence from diverse sources and leaves no aspect unattended while probing criminal behavioural cues, thus facilitating its applicability across varied forensic domains.
{"title":"Behavioural Evidence Analysis: A Paradigm Shift in Digital Forensics","authors":"Barkha Shree, Parneeta Dhaliwal","doi":"10.4018/IJDCF.20210901.OA2","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA2","url":null,"abstract":"Recent developments in digital forensics (DF) have emphasized that along with inspection of digital evidence, the study of behavioural clues based on behavioural evidence analysis (BEA) is vital for accurate and complete criminal investigation. This paper reviews the existing BEA approaches and process models and concludes the lack of standardisation in the BEA process. The research comprehends that existing BEA methodologies are restricted to specific characteristics of the forensic domain in question. To address these limitations, the paper proposes a standardised approach detailing the step-by-step implementation of BEA in the DF process. The proposed model presents a homogenous technique that can be practically applied to real-life cases. This standard BEA framework classifies digital evidence into categories to decipher associated offender characteristics. Unlike existing models, this new approach collects evidence from diverse sources and leaves no aspect unattended while probing criminal behavioural cues, thus facilitating its applicability across varied forensic domains.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"64 1","pages":"20-42"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78183032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA4
Rajashree Soman, R. Sukumar
Visitor validation at entrance generates a large number of image files that need to be transmitted over to cloud for future reference. The image data needs to be protected by active and passive adversaries from performing cryptographic attacks on these data. The image data also needs to be authenticated before giving it for future use. Focusing on reliable and secure image sharing, the proposed method involves building a novel cloud platform, which aims to provide a secure storage in the public cloud. The main objective of this paper is to provide a new way of secure image data storage and transmission on cloud using cryptographic algorithms. To overcome the flaws in current system, a novel method using BigchainDB, which has advantages of blockchain technology and traditional database, is proposed for storing attributes of image.
{"title":"Secure Storage and Sharing of Visitor Images Generated by Smart Entrance on Public Cloud","authors":"Rajashree Soman, R. Sukumar","doi":"10.4018/IJDCF.20210901.OA4","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA4","url":null,"abstract":"Visitor validation at entrance generates a large number of image files that need to be transmitted over to cloud for future reference. The image data needs to be protected by active and passive adversaries from performing cryptographic attacks on these data. The image data also needs to be authenticated before giving it for future use. Focusing on reliable and secure image sharing, the proposed method involves building a novel cloud platform, which aims to provide a secure storage in the public cloud. The main objective of this paper is to provide a new way of secure image data storage and transmission on cloud using cryptographic algorithms. To overcome the flaws in current system, a novel method using BigchainDB, which has advantages of blockchain technology and traditional database, is proposed for storing attributes of image.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"32 1","pages":"65-77"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79253264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA5
A. K. Mohan, Sethumadhavan Madathil, K. V. Lakshmy
Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.
{"title":"Holistic Analytics of Digital Artifacts: Unique Metadata Association Model","authors":"A. K. Mohan, Sethumadhavan Madathil, K. V. Lakshmy","doi":"10.4018/IJDCF.20210901.OA5","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA5","url":null,"abstract":"Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"4 1","pages":"78-100"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88748605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.4018/IJDCF.20210901.OA1
T. ChethanaH., Trisiladevi C. Nagavi
Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network’s final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.
{"title":"A New Framework for Matching Forensic Composite Sketches With Digital Images","authors":"T. ChethanaH., Trisiladevi C. Nagavi","doi":"10.4018/IJDCF.20210901.OA1","DOIUrl":"https://doi.org/10.4018/IJDCF.20210901.OA1","url":null,"abstract":"Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network’s final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":"43 1","pages":"1-19"},"PeriodicalIF":0.7,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74929855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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