• 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.

• 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.24 no.12
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.253-260
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• 2010

Communication devices aim to provide a multimedia service without spatial or temporal limitations in an IP-based environment. However, it is incapable of allowing for fair use by consumers who legally buy content, and damages provider contents through the indiscriminate distribution and use of illegal contents. The DRM system that emerged to solve this problem cannot protect licenses stored on communication devices, and manage licenses by redistribution. This paper proposes a license audit model, which checks for illegal access, modification and redistribution, and reports alert logs to the server.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.15-22
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• 2017

With the advance of Internet Technology, various social network services are created and used by users. Especially, the use of smart devices makes that multimedia contents can be used and distributed on social network services. However, since the crime rate also is increased by users with illegal purposes, there are needs to protect contents and block illegal usage of contents with multimedia forensics. In this paper, we propose a multimedia forensic technique which is identifying the video source. First, the scheme to acquire the sensor pattern noise (SPN) using morphology filtering is presented, which comes from the imperfection of photon detector. Using this scheme, the SPN of reference videos from the reference device is estimated and the SPN of an unknown video is estimated. Then, the similarity between two SPNs is measured to identify whether the unknown video is acquired using the reference device. For the performance analysis of the proposed technique, 30 devices including DSLR camera, compact camera, camcorder, action cam and smart phone are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 96% accuracy in identification.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.438-447
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• 2009

Digital home devices aims at providing the multimedia service which is not limited at time and space in home network environment. However, it is incapable of the fair use of consumers who legally buys contents, and causes damage to the contents providers owing to the indiscriminate distribution and use of illegal contents. DRM system appeared to solve this problem cannot protect the license stored on digital home devices and manage license by redistribution. This paper proposes a license audit model which makes an inspection of illegal access, modification and redistribution and reports alert logs to server.