• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1502-1505
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• 2015

디지털 처리 기술이 발전함에 따라 멀티미디어 장치 및 소프트웨어의 활용도가 증가하고 있다. 특히 이들 장치 및 소프트웨어는 저비용으로 고품질 및 고성능을 갖는 형태로 발전하고 있다. 그러나 본래의 의도와 다르게 불법적인 목적으로 디지털 획득 장치를 이용하는 범죄가 증가하고 있으며, 본 연구팀에서는 이를 차단하기 위하여 센서 패턴 노이즈를 이용한 디지털 영상 획득 장치 판별 기술을 연구해오고 있다. 최근 다양한 범죄에 있어서 동영상이 증거 자료로 활용되고 있으며, 본 논문에서는 동영상에 적용이 가능한 디지털 영상 획득 장치 기술을 연구하는데 있어서 동영상이 갖고 있는 프레임별 특성에 따른 판별 성능에 대하여 실험하고 분석하였다. 실시간으로 촬영한 동영상의 경우 B 프레임이 포함되지 않아, I 프레임과 P 프레임, 그리고 두 프레임을 함께 사용하여 각각에 대한 판별 성능에 대한 비교를 수행하였다. 그 결과 I 프레임과 P 프레임의 차이에 의한 성능의 차이는 있었으나, 장치 판별에 대한 영향은 충분히 무시할 수 있음을 확인할 수 있었다.

• 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.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.1-9
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• 2019

With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.