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A Method for De-Identification Analysis of Encrypted Video

A Method for De-Identification Analysis of Encrypted Video

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Deok-Han Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea

As the variety of video content increases, so does the likelihood of data breaches of sensitive personal information. Consequently, several video de-identification techniques have been developed to protect personal data, alongside ongoing research into video encryption technology, which offers the advantage of decryption to obtain the original video without storing a separate video copy. While it is possible to visually evaluate whether individual encrypted frames have been correctly de-identified, human interpretation makes this method vulnerable to subjective bias. Many video encryption studies employ traditional image quality evaluation metrics, such as peak signal-to-noise ratio and structural similarity index measure (SSIM), to objectively demonstrate the de-identification of encrypted videos. However, these metrics prove to be insufficiently effective for this purpose. These image metrics can measure the de-identification rate of a single frame in encrypted videos, but not the entire video. This study introduces a quantitative analysis method to confirm de-identification in fully encrypted videos. In this study, we measured the de-identification rates of all frames of encrypted videos using a weighted sum of SSIM and edge differential ratio. We also measured the de-identification rate of a fully encrypted video. Finally, we graphically analyzed these data using line charts. This objective methodology enabled the identification of portions of the encrypted video where de-identification techniques may not have been appropriately applied.

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A Method for Quantitative Object De-Identification Analysis of Anonymized Video

A Method for Quantitative Object De-Identification Analysis ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Deok-Han Kim Yujun Kim Young-Gab Kim Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The rise in video content has amplified the risk of information leakage, prompting the development of various anonymization techniques. These techniques include traditional methods such as pixelation, blurring, and masking, as well as more advanced approaches like video encryption, which involves encrypting parameters during video encoding, and techniques that subtly alter facial features to prevent identification. However, assessing the effectiveness of these anonymization techniques remains challenging. Common metrics such as the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), often used for object anonymization in videos, primarily assess image quality and lack accuracy for security evaluation. Furthermore, subjective human evaluations fail to provide the consistent, quantitative data necessary for cross-study comparisons. Given that a single identifiable frame in a video can compromise overall security, this study introduces a quantitative method for evaluating the de-identification rate in regions of interest (ROI) within videos. The proposed approach calculates an object de-identification rate (ODR) by combining SSIM and edge detection ratio (EDR) using a harmonic mean and a power mean. Our proposed method enhances the precision and reliability for security of anonymized video.

关键词： Measurement Data privacy Privacy PSNR Power system harmonics Video surveillance Information filtering Encryption Reliability Information integrity

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Emot Act AR: Tailoring Content Through User Emotion and Activity Analysis

Emot Act AR: Tailoring Content Through User Emotion and Acti...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Somaiieh Rokhsaritalemi Abolghasem Sadeghi-Niaraki Soo-Mi Choi Sejong University Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In an era characterized by immersive content delivery experiences, the convergence of augmented reality (AR) and user behavior data stands out as a transformative synergy. This paper introduces “Emot Act AR,” an innovative system intricately designed to dynamically tailor virtual content by user data. This framework incorporates user emotions and activities as intrinsic components. Through the utilization of sensing devices like camera and advanced AI models, the system dynamically customizes virtual elements, such as virtual flowers, in both static and dynamic settings. Additionally, the system utilizes user activity data to activate messaging avatars that encourage exercise.

关键词： Solid modeling Three-dimensional displays Conferences Avatars User interfaces Flowering plants Cameras

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Efficient Automatic Modulation Classification for Next-Generation Wireless Networks

TechRxiv

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TechRxiv 2023年

作者： To, Truong An Puspitasari, Annisa Anggun Lee, Byung Moo The Department of Intelligent Mechatronics Engineering Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

This paper introduces a novel automatic modulation classification (AMC) algorithm for wireless communication systems. With the advent of sixth-generation (6G) networks, the demand for high-accuracy and computationally-efficient AMC algorithms has become increasingly pressing. In response to this need, we propose a new model called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines a threshold denoise (TD) module and a recurrent neural network (RNN) module to achieve high accuracy and fast computation times. The TD module reduces the received signal’s noise level, while the RNN module performs the modulation classification on the denoised signal. The proposed TDRNN algorithm is evaluated on various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN algorithm outperforms existing methods in terms of accuracy, speed, and computational complexity. The TDRNN algorithm is suitable for adaptive coding and modulation in 6G wireless communication systems. © 2023, CC BY.

关键词： Recurrent neural networks

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Trusted and Privacy-Preserving Billing System for Smart Grids

Trusted and Privacy-Preserving Billing System for Smart Grid...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Kayumov Abduaziz Shincheol Lee Ji Sun Shin Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea Department of Computer and Information Security Sejong University Seoul South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

With the advent of smart meters in recent years, privacy has become one of the biggest concerns in smart grids. While privacy is achieved by designing smart grid services while maintaining the anonymous participation of consumers, several technical issues must be solved by electricity companies, such as confirmation of payments or assurance of consumption data integrity and quality. In this paper, we propose a new system model for data aggregation in smart grid environments that balances the needs of consumers and electricity companies. To decentralize and achieve transparent data processing, we apply a permissioned blockchain network and smart contracts. To support consumer privacy, we propose a privacy-preserving smart contract that runs on a trusted execution environment. Payments are processed and confirmed at the end of a billing period by the smart contract to enforce data consistency and integrity. We implement our smart contract design for Hyperledger Fabric, a permissioned blockchain network, and analyze its privacy, data, and blockchain benefits.

关键词： Privacy Data privacy Electricity Data integrity Smart contracts Companies Smart meters Smart grids Blockchains Reliability

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DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization

DeRO: Dead Reckoning Based on Radar Odometry With Accelerome...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Hoang Viet Do Yong Hun Kim Joo Han Lee Min Ho Lee Jin Woo Song Department of Convergence Engineering for Intelligent Drone Intelligent Navigation and Control Systems Laboratory (iNCSL) School of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we propose a radar odometry structure that directly utilizes radar velocity measurements for dead reckoning while maintaining its ability to update estimations within the Kalman filter framework. Specifically, we employ the Doppler velocity obtained by a 4D Frequency Modulated Continuous Wave (FMCW) radar in conjunction with gyroscope data to calculate poses. This approach helps mitigate high drift resulting from accelerometer biases and double integration. Instead, tilt angles measured by gravitational force are utilized alongside relative distance measurements from radar scan matching for the filter’s measurement update. Additionally, to further enhance the system’s accuracy, we estimate and compensate for the radar velocity scale factor. The performance of the proposed method is verified through five real-world open-source datasets. The results demonstrate that our approach reduces position error by 62% and rotation error by 66% on average compared to the state-of-the-art radar-inertial fusion method in terms of absolute trajectory error.

关键词： Accelerometers Matched filters Dead reckoning Radar measurements Estimation Doppler radar Gyroscopes Odometry Doppler effect Velocity measurement

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Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

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computer Systems Science & engineering 2023年第11期47卷 1633-1650页

作者： T.Satyanarayana Murthy Kollati Vijaya Kumar Fayadh Alenezi E.Laxmi Lydia Gi-Cheon Park Hyoung-Kyu Song Gyanendra Prasad Joshi Hyeonjoon Moon Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of International Affairs and Education Gangseo UniversitySeoul07661Korea Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from ***,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing ***,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human *** automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human ***’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several *** the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)*** the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the *** addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature *** fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of *** performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit *** comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

关键词： Fruit classification computer vision machine learning deep learning metaheuristics

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A Reinforcement Learning-Based Computational Model of Human Elbow Joint Operation for Effective Human-Machine Interface

A Reinforcement Learning-Based Computational Model of Human ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Yoo, Eunsoo Choi, Kyungrak Park, Hangue Sungkyunkwan University Department of MetaBioHealth Suwon Korea Republic of Texas A & M University Department of Electrical and Computer Engineering United States Sungkyunkwan University Department of Biomedical Engineering Department of Intelligent Precision Healthcare Convergence Department of MetaBioHealth Suwon Korea Republic of

ISBN: (纸本)9798331530839

Designing effective human-machine interfaces requires understanding complex interactions between humans and machines. However, even bi-directional communication that relies on sensor data is often insufficient due to the unpredictability of human behaviors. To better anticipate these behaviors, a computational model that reflects the operating principles of the human nervous system is essential. This study presents a computational neuromechanical model that focuses on human sensorimotor operations, particularly in the context of elbow joint rotation. The model leverages reinforcement learning (RL) to simulate the brain's reward mechanism in motor adjustment, with the reward function adjusted by a multiplication constant (M) that reflects individual variability in sensorimotor processing. Measurement and simulation data were evaluated based on their overlap ratios. The test results demonstrate that the RL-based model, when calibrated with an optimal M value, closely matches with measured motor outputs, indicating its potential for improving the effectiveness of the human-machine interface. © 2024 IEEE.

关键词： Reinforcement learning

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Model-Based Quadrotor Position Boundary Prediction Considering Temporary Disconnection with Ground Control System

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Journal of Institute of Control, Robotics and Systems 2022年第2期28卷 202-208页

作者： Kang, San Hee Kim, Eung Ju Kwon, Yeong Seo Kim, Yong Hun Song, Jin Woo Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Korea Republic of

High-efficiency communication methods, such as beamforming, are used for high-capacity data transmission—from UAVs (Unmanned Aerial Vehicles) to the ground in real-time. However, the directivity of beamforming and high mobility of the quadrotor necessitate continuous positioning to calculate optimal channels. Herein, we propose a quadrotor position boundary prediction method using a model-based filter for tracking a quadrotor with temporary disconnection. For accurate model-based state prediction, the parameters of the quadrotor are estimated using a model-based extended Kalman filter when communication is possible at the beginning of the flight. If communication with the quadrotor is not reliable, the estimated model parameters and dynamic models are used to predict the position and attitude of the quadrotor and calculate a boundary where the quadrotor can be located. The proposed algorithm was verified using Monte Carlo simulation. Simulations confirmed that the actual quadrotor trajectory was within the expected boundaries for position error. © ICROS 2022.

关键词： Forecasting

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