• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1263-1272
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• 2004

In this paper, we present a novel video fingerprinting implementation method to identify the source of illegal copies. The video fingerprinting is achieved by the insertion of uniform distributed random number is made by seller and buyer's identification key-in the video wavelet coefficients by their temporal wavelet transform. The proposed fingerprinting is able to detect unique fingerprint of video contents even if they have been distorted by collusion attacks and MPEG2 compression. Especially, we use characteristics of the temporal wavelet transform to assign user's embedding area. Experimental results show the traceability of unauthorized distribution of video contents and its robustness to various collusion attacks and MPEG2 compression.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1074-1083
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• 2011

Noise of digital camera has been noticeable to its users. Particularly, noise of a barrel assembly module in zoom in/zoom out operation is recorded while taking a video. Reduction of barrel noise becomes crucial but there are not many studies on noise of digital camera due to its short history of use. In this study, experiment-based analyses are implemented to identify sources of noise and vibration because of complexity and compactness of the barrel system. Output noise is acquired in various operation conditions using synchronization for spectral analysis. Noise sources of a barrel assembly in zoom operating are first identified by the comparison with gear frequency analysis and then correlation analysis between noise and vibration is applied to confirm the generation path of noise. Analysis on noise transfer characteristic of zoom module is also carried out in order to identify the most contributing components. One of possible countermeasures of noise in zoom operating is investigated by an experimental approach.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.885-894
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• 2016

As smartphones become more common, anybody can take pictures and record videos easily nowadays. Video files taken from smartphones can be used as important clues and evidence. While you analyze video files taken from smartphones, there are some occasions where you need to prove that a video file was recorded by a specific smartphone. To do this, you can utilize various fingerprint techniques mentioned in existing research. But you might face the situation where you have to strengthen the result of fingerprinting or fingerprint technique can't be used. Therefore forensic investigation of the smartphone must be done before fingerprinting and the database of metadata of video files should be established. The artifacts in a smartphone after video recording and the database mentioned above are discussed in this paper.

• Electrical & Electronic Materials
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• v.8 no.5
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• pp.633-639
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• 1995

Dropouts in magnetic media are a primary quality deficiency which is detrimental to magnetic signal quality and thus the major impediment to error-free recording, especially in high density digital recorders. The specific form of defects and causes found to be responsible for signal dropouts occurring in magnetic tape were studied. Dropout occurred when the RF signal falls to low level due to the various types of surface defects. However, the fall in the level of the RF signal in gravure roll coated tapes was most often caused by foreign particles adhering to the magnetic tape. It was also shown from the present study that scattered particles trapped under the tape surface or put on the top can lift it as it crosses the head, creating a spherical tent shaped defect and causing a temporary signal drop. Dropout producing substances were identified through optical and microchemical analyses. The results were correlated with measured electrical signal losses combined with analytical microscopy to clarify source identification of defects.