The Experts below are selected from a list of 30669 Experts worldwide ranked by ideXlab platform
Li Liu - One of the best experts on this subject based on the ideXlab platform.
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countering anti Forensics of median filtering
2014Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li LiuAbstract:The statistical fingerprints left by median filtering can be a valuable clue for image Forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-Forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-Forensics of median filtering.
Hui Zeng - One of the best experts on this subject based on the ideXlab platform.
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countering anti Forensics of median filtering
2014Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li LiuAbstract:The statistical fingerprints left by median filtering can be a valuable clue for image Forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-Forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-Forensics of median filtering.
Tengfei Qin - One of the best experts on this subject based on the ideXlab platform.
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countering anti Forensics of median filtering
2014Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li LiuAbstract:The statistical fingerprints left by median filtering can be a valuable clue for image Forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-Forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-Forensics of median filtering.
Xiangui Kang - One of the best experts on this subject based on the ideXlab platform.
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a multi purpose image counter anti forensic method using convolutional neural networks
2016Co-Authors: Jingjing Yu, Yifeng Zhan, Jianhua Yang, Xiangui KangAbstract:During the past decade, image Forensics has made rapid progress due to the growing concern of image content authenticity. In order to remove or conceal the traces that Forensics based on, some farsighted forgers take advantage of so-called anti-Forensics to make their forgery more convincing. To rebuild the credibility of Forensics, many countermeasures against anti-Forensics have been proposed. This paper presents a multi-purpose approach to detect various anti-Forensics based on the architecture of Convolutional Neural Networks (CNN), which can automatically extract features and identify the forged types. Our model can detect various image anti-Forensics both in binary and multi-class decision effectively. Experimental results show that the proposed method performs well for multiple well-known image anti-forensic methods.
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countering anti Forensics of median filtering
2014Co-Authors: Hui Zeng, Xiangui Kang, Tengfei Qin, Li LiuAbstract:The statistical fingerprints left by median filtering can be a valuable clue for image Forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-Forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-Forensics of median filtering.
Suleman Khan - One of the best experts on this subject based on the ideXlab platform.
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network Forensics review taxonomy and open challenges
2016Co-Authors: Suleman Khan, Abdullah Gani, Ainuddin Wahid Abdul Wahab, Muhammad Shiraz, Iftikhar AhmadAbstract:In recent years, a number of network Forensics techniques have been proposed to investigate the increasing number of cybercrimes. Network Forensics techniques assist in tracking internal and external network attacks by focusing on inherent network vulnerabilities and communication mechanisms. However, investigation of cybercrime becomes more challenging when cyber criminals erase the traces in order to avoid detection. Therefore, network Forensics techniques employ mechanisms to facilitate investigation by recording every single packet and event that is disseminated into the network. As a result, it allows identification of the origin of the attack through reconstruction of the recorded data. In the current literature, network Forensics techniques are studied on the basis of forensic tools, process models and framework implementations. However, a comprehensive study of cybercrime investigation using network Forensics frameworks along with a critical review of present network Forensics techniques is lacking. In other words, our study is motivated by the diversity of digital evidence and the difficulty of addressing numerous attacks in the network using network Forensics techniques. Therefore, this paper reviews the fundamental mechanism of network Forensics techniques to determine how network attacks are identified in the network. Through an extensive review of related literature, a thematic taxonomy is proposed for the classification of current network Forensics techniques based on its implementation as well as target data sets involved in the conducting of forensic investigations. The critical aspects and significant features of the current network Forensics techniques are investigated using qualitative analysis technique. We derive significant parameters from the literature for discussing the similarities and differences in existing network Forensics techniques. The parameters include framework nature, mechanism, target dataset, target instance, forensic processing, time of investigation, execution definition, and objective function. Finally, open research challenges are discussed in network Forensics to assist researchers in selecting the appropriate domains for further research and obtain ideas for exploring optimal techniques for investigating cyber-crimes.
