• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3268-3283
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• 2018

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1256-1261
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• 2008

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When multi-point is measure by using a pattern recognition, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.675-680
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• 2008

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When displacement is measure by using camera images, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.

• Transactions on Electrical and Electronic Materials
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• v.8 no.5
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• pp.196-200
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• 2007

In this paper, we propose and discuss the design using a simple method that reduces the fixed pattern noise(FPN) generated on the amorphous Si($\alpha-Si$) bolometer. This method is applicable to an IR image sensor. This method can also minimize the size of the reference resistor in the readout integrated circuit(ROIC) which processes the signal of an IR image sensor. By connecting four bolometer cells in parallel and averaging the resistances of the bolometer cells, the fixed pattern noise generated in the bolometer cell due to process variations is remarkably reduced. Moreover an $\alpha-Si$ bolometer cell, which is made by a MEMS process, has a large resistance value to guarantee an accurate resistance value. This makes the reference resistor be large. In the proposed cell structure, because the bolometer cells connected in parallel have a quarter of the original bolometer's resistance, a reference resistor, which is made by poly-Si in a CMOS process chip, is implemented to be the size of a quarter. We designed a ROIC with the proposed cell structure and implemented the circuit using a 0.35 um CMOS process.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.561-570
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• 2015

With the advance of IT technology, contents from digital multimedia devices and softwares are widely used and distributed. However, novice uses them for illegal purpose and hence there are needs for protecting contents and blocking illegal usage through multimedia forensics. In this paper, we present a forensic technique for identifying digital imaging source using sensor pattern noise. First, the way to acquire the sensor pattern noise which comes from the imperfection of photon detector against light is presented. Then, the way to identify the similarity of digital imaging sources is explained after estimating the sensor pattern noises from the reference images and the unknown image. For the performance analysis of the proposed technique, 10 devices including DSLR camera, compact camera, smartphone and camcorder are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 99.6% identification accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2153-2158
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• 2016

Multimedia such as image, audio, and video is easy to create and distribute with the advance of IT. Since novice uses them for illegal purposes, multimedia forensics are required to protect contents and block illegal usage. This paper presents a multimedia forensic algorithm for video to identify the device used for acquiring unknown video files. First, the way to calculate a sensor pattern noise using Wiener filter (W-SPN) is presented, which comes from the imperfection of photon detectors against light. Then, the way to identify the device is explained after estimating W-SPNs from the reference device and the unknown video. For the experiment, 30 devices including DSLR, compact camera, smartphone, and camcorder are tested and analyzed quantitatively. Based on the results, the presented algorithm can achieve the 96.0% identification accuracy.

• Transactions of the Society of Information Storage Systems
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• v.5 no.2
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• pp.89-95
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• 2009

We setup the system to measure the noise characteristics of the 5M complementary metal-oxide semiconductor (CMOS) image sensor by generic measurement indicator of Standard mobile imaging architecture (SMIA) which is one of internal standard of mobile imaging architecture. To evaluate the effect of environment and setting parameters, such as temperature and integration time, we measure the variation of the dark signal, dynamic range and fixed pattern noise of image sensor. We also detect the number of defective pixels and cluster defects defined as adjacent single defect pixels at 5M CMOS image sensor. Then, we find the existence of some cluster defects in experiment, which are not expected in calculation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.7-8
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• 2009

In the presence of infrared light, a CMOS Readout IC (ROIC) for a microbolometer typed infrared sensor detects the voltage or current that is caused by the changing in resistance in the bolometer sensor. A serious problem in designing the ROIC is how the value of the bolometer and reference resistors vary because of variations in manufacturing process. Since different pixel have different, resistance values, sensor operations must contend with fixed pattern noise (FPN) problems. In this paper, we propose a novel technique to compensate for the fluctuation in reference resistance by tiling into account the process variation. By using constant current source basing and correlated double sampling, we solved FPN.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.11
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• pp.857-861
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• 2012

FPN (fixed-pattern-noise) mainly comes from the device or pattern mismatches in pixel and color filter, pixel photodiode leakage in CMOS image sensor. In this paper, optical stack module related pixel FPN was investigated and the classification of pixel FPN contribution with the individual optical module process was presented. The methodology and procedure would be helpful in reducing the greater pixel FPN and distinguishing the complex FPN sources with respect to various noise factors.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.2
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• pp.119-122
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• 2009

As infrared light radiates, the CMOS Readout IC (ROIC) for the microbolometer typed infrared sensor detects voltage or current which is caused by the variation of resistance in the bolometer sensor. A serious problem we may have in designing the ROIC is the value of bolometer and reference resistors will be changed due to process variation. Since each pixel does not have the same value of resistance, fixed pattern noise problems happen during the sensor operations. In this paper, we propose a novel technique to compensate the fluctuation of reference resistance with taking account of process variation. By using a comparator and a cross coupled latch, we will make the value of reference resistor same as the bolometer's.