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International Journal of Digital Crime and Forensics最新文献

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Coverless Information Hiding Based on WGAN-GP Model 基于WGAN-GP模型的无覆盖信息隐藏
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-07-01 DOI: 10.4018/IJDCF.20210701.OA5
X. Duan, Baoxia Li, Daidou Guo, Kai Jia, E. Zhang, Chuan Qin
{"title":"Coverless Information Hiding Based on WGAN-GP Model","authors":"X. Duan, Baoxia Li, Daidou Guo, Kai Jia, E. Zhang, Chuan Qin","doi":"10.4018/IJDCF.20210701.OA5","DOIUrl":"https://doi.org/10.4018/IJDCF.20210701.OA5","url":null,"abstract":"Steganalysis technology judges whether there is secret information in the carrier by monitoring the abnormality of the carrier data, so the traditional information hiding technology has reached the bottleneck. Therefore, this paper proposed the coverless information hiding based on the improved training of Wasserstein GANs (WGAN-GP) model. The sender trains the WGAN-GP with a natural image and a secret image. The generated image and secret image are visually identical, and the parameters of generator are saved to form the codebook. The sender uploads the natural image (disguise image) to the cloud disk. The receiver downloads the camouflage image from the cloud disk and obtains the corresponding generator parameter in the codebook and inputs it to the generator. The generator outputs the same image for the secret image, which realized the same results as sending the secret image. The experimental results indicate that the scheme produces high image quality and good security.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77309513","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}
引用次数: 3
Lossless Data Hiding in LWE-Encrypted Domains Based on Key-Switching 基于密钥交换的lwe加密域无损数据隐藏
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-07-01 DOI: 10.4018/IJDCF.20210701.OA6
Tingting Su, Yan Ke, Yi Ding, Jia Liu
{"title":"Lossless Data Hiding in LWE-Encrypted Domains Based on Key-Switching","authors":"Tingting Su, Yan Ke, Yi Ding, Jia Liu","doi":"10.4018/IJDCF.20210701.OA6","DOIUrl":"https://doi.org/10.4018/IJDCF.20210701.OA6","url":null,"abstract":"This paper proposes a lossless data hiding scheme in learning with errors (LWE)-encrypted domain based on key-switching technique. Lossless data hiding and extraction could be realized by a third party without knowing the private key for decryption. Key-switching-based least-significant-bit (KSLSB) data hiding method has been designed during the lossless data hiding process. The owner of the plaintext first encrypts the plaintext by using LWE encryption and uploads ciphertext to a (trusted or untrusted) third server. Then the server performs KSLSB to obtain a marked ciphertext. To enable the third party to manage ciphertext flexibly and keep the plaintext secret, the embedded data can be extracted from the marked ciphertext without using the private key of LWE encryption in the proposed scheme. Experimental results demonstrate that data hiding would not compromise the security of LWE encryption, and the embedding rate is 1 bit per bit of plaintext without introducing any loss into the directly decrypted result.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76392022","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}
引用次数: 0
Information Hiding Model Based on Channel Construction of Orthogonal Basis 基于正交基信道构造的信息隐藏模型
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-05-01 DOI: 10.4018/IJDCF.20210501.OA1
Bao Kangsheng
{"title":"Information Hiding Model Based on Channel Construction of Orthogonal Basis","authors":"Bao Kangsheng","doi":"10.4018/IJDCF.20210501.OA1","DOIUrl":"https://doi.org/10.4018/IJDCF.20210501.OA1","url":null,"abstract":"Secret information communication model based on channel construction of orthogonal basis can implement secret information hiding and recovery without a key. The orthogonal basis is constructed by the media carrier's self-correlation. Carrier and secret information channels are constructed independently. And it has good properties of avoiding detection. The experiments show that the model with proper carrier components and threshold of secret information coding has the capacity of secret information and robustness. And the secret information capacity and anti-noise capability can be improved by compressed and error correcting or checking codes.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85664075","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}
引用次数: 0
Color Image Encryption Using Angular Graph Fourier Transform 利用角图傅里叶变换进行彩色图像加密
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-05-01 DOI: 10.4018/IJDCF.20210501.OA5
Liu Yang, W. Meng, Xudong Zhao
{"title":"Color Image Encryption Using Angular Graph Fourier Transform","authors":"Liu Yang, W. Meng, Xudong Zhao","doi":"10.4018/IJDCF.20210501.OA5","DOIUrl":"https://doi.org/10.4018/IJDCF.20210501.OA5","url":null,"abstract":"In this paper, an angular graph Fourier transform (AGFT) is introduced to encrypt color images with their intrinsic structures. The graph Fourier transform (GFT) is extended to the AGFT and proven to have the desired properties of angular transform and graph transform. In the proposed encryption method, color images are encoded by DNA sequences and confused under the control of chaotic key streams firstly. Secondly, sparse decomposition based on the random walk is applied to scramble pixels spatially, and a series of sub-images are obtained. This step increases encryption efficiency. Finally, the intrinsic sub-image structure is reflected by graphs, and the signals on different subgraphs are transformed into different AGFT domains with particular angular parameters, which makes the proposed method relevant to the original image structure and enhances security. The experimental results demonstrate that the proposed algorithm can resist various potential attacks and achieve better performance than the state-of-the-art algorithms.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84035063","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}
引用次数: 0
A HEVC Video Steganalysis Against DCT/DST-Based Steganography 针对DCT/ dst隐写的HEVC视频隐写分析
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-05-01 DOI: 10.4018/IJDCF.20210501.OA2
Henan Shi, Tanfeng Sun, Xinghao Jiang, Yi Dong, Ke Xu
{"title":"A HEVC Video Steganalysis Against DCT/DST-Based Steganography","authors":"Henan Shi, Tanfeng Sun, Xinghao Jiang, Yi Dong, Ke Xu","doi":"10.4018/IJDCF.20210501.OA2","DOIUrl":"https://doi.org/10.4018/IJDCF.20210501.OA2","url":null,"abstract":"The development of video steganography has put forward a higher demand for video steganalysis. This paper presents a novel steganalysis against discrete cosine/sine transform (DCT/DST)-based steganography for high efficiency video coding (HEVC) videos. The new steganalysis employs special frames extraction (SFE) and accordion unfolding (AU) transformation to target the latest DCT/DST domain HEVC video steganography algorithms by merging temporal and spatial correlation. In this article, the distortion process of DCT/DST-based HEVC steganography is firstly analyzed. Then, based on the analysis, two kinds of distortion, the intra-frame distortion and the inter-frame distortion, are mainly caused by DCT/DST-based steganography. Finally, to effectively detect these distortions, an innovative method of HEVC steganalysis is proposed, which gives a combination feature of SFE and a temporal to spatial transformation, AU. The experiment results show that the proposed steganalysis performs better than other methods.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90636688","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}
引用次数: 2
Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples 基于虚拟样本生成和集成学习的小训练样本图像源识别
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-05-01 DOI: 10.4018/IJDCF.20210501.OA3
Shiqi Wu, Bo Wang, Jianxiang Zhao, Mengnan Zhao, Kun Zhong, Yanqing Guo
{"title":"Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples","authors":"Shiqi Wu, Bo Wang, Jianxiang Zhao, Mengnan Zhao, Kun Zhong, Yanqing Guo","doi":"10.4018/IJDCF.20210501.OA3","DOIUrl":"https://doi.org/10.4018/IJDCF.20210501.OA3","url":null,"abstract":"Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86203893","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}
引用次数: 5
A Modification-Free Steganography Algorithm Based on Image Classification and CNN 基于图像分类和CNN的无修改隐写算法
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-05-01 DOI: 10.4018/IJDCF.20210501.OA4
Jianbin Wu, Yang Zhang, Chuwei Luo, L. Yuan, X. Shen
{"title":"A Modification-Free Steganography Algorithm Based on Image Classification and CNN","authors":"Jianbin Wu, Yang Zhang, Chuwei Luo, L. Yuan, X. Shen","doi":"10.4018/IJDCF.20210501.OA4","DOIUrl":"https://doi.org/10.4018/IJDCF.20210501.OA4","url":null,"abstract":"In order to improve the data-embedding capacity of modification-free steganography algorithm, scholars have done a lot of research work to meet practical demands. By researching the user's behavioral habits of several social platforms, a semi-structured modification-free steganography algorithm is introduced in the paper. By constructing the mapping relationship between small icons and binary numbers, the idea of image stitching is utilized, and small icons are stitched together according to the behavioral habits of people's social platforms to implement the graphical representation of secret messages. The convolutional neural network (CNN) has been used to train the small icon recognition and classification data set in the algorithm. In order to improve the robustness of the algorithm, the icons processed by various attack methods are introduced as interference samples in the training set. The experimental results show that the algorithm has good anti-attack ability, and the hiding capacity can be improved, which can be used in the covert communication.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90405480","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}
引用次数: 1
Detection of Phishing in Internet of Things Using Machine Learning Approach 基于机器学习方法的物联网网络钓鱼检测
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-03-01 DOI: 10.4018/IJDCF.2021030101
Sameena Naaz
{"title":"Detection of Phishing in Internet of Things Using Machine Learning Approach","authors":"Sameena Naaz","doi":"10.4018/IJDCF.2021030101","DOIUrl":"https://doi.org/10.4018/IJDCF.2021030101","url":null,"abstract":"Phishing attacks are growing in the similar manner as e-commerce industries are growing. Prediction and prevention of phishing attacks is a very critical step towards safeguarding online transactions. Data mining tools can be applied in this regard as the technique is very easy and can mine millions of information within seconds and deliver accurate results. With the help of machine learning algorithms like random forest, decision tree, neural network, and linear model, we can classify data into phishing, suspicious, and legitimate. The devices that are connected over the internet, known as internet of things (IoT), are also at very high risk of phishing attack. In this work, machine learning algorithms random forest classifier, support vector machine, and logistic regression have been applied on IoT dataset for detection of phishing attacks, and then the results have been compared with previous work carried out on the same dataset as well as on a different dataset. The results of these algorithms have then been compared in terms of accuracy, error rate, precision, and recall.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88824644","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}
引用次数: 10
Research on Threat Information Network Based on Link Prediction 基于链路预测的威胁信息网络研究
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-03-01 DOI: 10.4018/IJDCF.2021030106
Jin Du, Feng Yuan, Liping Ding, Guangxuan Chen, Xuehua Liu
{"title":"Research on Threat Information Network Based on Link Prediction","authors":"Jin Du, Feng Yuan, Liping Ding, Guangxuan Chen, Xuehua Liu","doi":"10.4018/IJDCF.2021030106","DOIUrl":"https://doi.org/10.4018/IJDCF.2021030106","url":null,"abstract":"The study of complex networks is to discover the characteristics of these connections and to discover the nature of the system between them. Link prediction method is a classic in the study of complex networks. It ca not only reflect the relationship between the node similarity. More can be estimated through the edge, which reveals the intrinsic factors of network evolution, namely the network evolution mechanism. Threat information network is the evolution and development of the network. The introduction of such a complex network of interdisciplinary approach is an innovative research perspective to observe that the threat intelligence occurs. The characteristics of the network show, at the same time, also can predict what will happen. The evolution of the network for network security situational awareness of the research provides a new approach.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84650927","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}
引用次数: 1
An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment 基于改进萤火虫算法的云环境入侵检测系统
IF 0.7
International Journal of Digital Crime and Forensics Pub Date : 2021-03-01 DOI: 10.4018/IJDCF.2021030105
Partha Ghosh, D. Sarkar, Joy Sharma, S. Phadikar
{"title":"An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment","authors":"Partha Ghosh, D. Sarkar, Joy Sharma, S. Phadikar","doi":"10.4018/IJDCF.2021030105","DOIUrl":"https://doi.org/10.4018/IJDCF.2021030105","url":null,"abstract":"The present era is being dominated by cloud computing technology which provides services to the users as per demand over the internet. Satisfying the needs of huge people makes the technology prone to activities which come up as a threat. Intrusion detection system (IDS) is an effective method of providing data security to the information stored in the cloud which works by analyzing the network traffic and informs in case of any malicious activities. In order to control high amount of data stored in cloud, data is stored as per relevance leading to distributed computing. To remove redundant data, the authors have implemented data mining process such as feature selection which is used to generate an optimum subset of features from a dataset. In this paper, the proposed IDS provides security working upon the idea of feature selection. The authors have prepared a modified-firefly algorithm which acts as a proficient feature selection method and enables the NSL-KDD dataset to consume less storage space by reducing dimensions as well as less training time with greater classification accuracy.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78566042","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}
引用次数: 9
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