• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.73-83
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• 2014

Thanks to the development of diverse audio editing Technology, audio file can be easily revised. As a result, diverse social problems like forgery may be caused. Digital forensic technology is actively studied to solve these problems. In this paper, a hand-held device identification method, an area of digital forensic technology is proposed. It uses the noise features of devices caused by the design and the integrated circuit of each device but cannot be identified by the audience. Wiener filter is used to get the noise sounds of devices and their acoustic features are extracted via MIRtoolbox and then they are trained by multi-layer neural network. To evaluate the proposed method, we use 5-fold cross-validation for the recorded data collected from 6 mobile devices. The experiments show the performance 99.9%. We also perform some experiments to observe the noise features of mobile devices are still useful after the data are uploaded to UCC. The experiments show the performance of 99.8% for UCC data.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1450-1456
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• 2009

By the explosive growth of IT technologies, mobile phones have embedded a lot of functions and everyone can use them with facility. But there are various cybercrimes as invasions of one's privacy or thefts of company's sensitive information using a built-in digital camera function in a mobile phone. In this paper, we propose a scheme for analyzing evidences by digital pictures on mobile phones. Therefore we analyze the features of digital pictures on mobile phones and make databases of characteristic patterns based on the vendor and the model of mobile phone. The proposed scheme will help to acquire digital evidences by providing a better decision of the vendor and/or the model of mobile phone by cybercrime suspects.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.365-372
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• 2007

The information of world is most likely to be created as digital data. These digital productions need some legal protection mechanisms or techniques because users can illegally use them. Thus many researchers are developing various techniques. Currently most techniques are focusing on the physical and chemical methods like disk inspection for taking legal evidence about production infringement. This paper has developed a computer forensics-based copyrights protection system capable of detecting and notifying disobedience facts when user uses illegally a production. Furthermore if the user infringes continually the production the system stores the infringement facts to take the legal evidence by mapping to law for intellectual property right. The technique can protect data from digital evidence manipulation or destruction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.397-403
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• 2020

With the convenience of real-time conversation, multimedia file and contact sharing services, most people use instant messenger, and its usage time is increasing. Because the messengers contain a lot of user behavior information data, in the digital forensic investigation, they can be very useful evidence to identify user behavior. However, some of useful data can be difficult to acquire or recognize because they are encrypted or deleted. Thus, in order to use the messenger data as evidence, the study of message decryption process and message recovery is essential. In this paper, we analyze the database encryption process of the instant messenger, MalangMalang Talkafe, and propose the method to decrypt it. In addition, we propose the methods to identify the deleted messages and recover from the volatile memory area.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.411-420
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• 2019

Camera model identification has been a subject of steady study in the field of digital forensics. Among the increasingly sophisticated crimes, crimes such as illegal filming are taking up a high number of crimes because they are hard to detect as cameras become smaller. Therefore, technology that can specify which camera a particular image was taken on could be used as evidence to prove a criminal's suspicion when a criminal denies his or her criminal behavior. This paper proposes a deep learning model to identify the camera model used to acquire the image. The proposed model consists of four convolution layers and two fully connection layers, and a high pass filter is used as a filter for data pre-processing. To verify the performance of the proposed model, Dresden Image Database was used and the dataset was generated by applying the sequential partition method. To show the performance of the proposed model, it is compared with existing studies using 3 layers model or model with GLCM. The proposed model achieves 98% accuracy which is similar to that of the latest technology.