• Title/Summary/Keyword: Image Privacy

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CCTV Video Privacy Protection Scheme Based on Edge Blockchain (엣지 블록체인 기반의 CCTV 영상 프라이버시 보호 기법)

  • Lee, Donghyeok;Park, Namje
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.10
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    • pp.101-113
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    • 2019
  • Recently, the intelligent video surveillance technology has become able to provide various services such as predictive surveillance that have not been provided previously. Securing the security of the intelligent video surveillance is essential, and malicious manipulation of the original CCTV video data can lead to serious social problems. Therefore, in this paper, we proposed an intelligent video surveillance environment based on blockchain. The proposed scheme guarantees the integrity of the CCTV image data and protects the ROI privacy through the edge blockchain, so there is no privacy exposure of the object. In addition, it is effective because it is possible to increase the transmission efficiency and reduce storage by enabling video deduplication.

Lossless Deformation of Brain Images for Concealing Identification (신원 은닉을 위한 두뇌 영상의 무손실 변경)

  • Lee, Hyo-Jong;Yu, Du Ruo
    • The KIPS Transactions:PartB
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    • v.18B no.6
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    • pp.385-388
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    • 2011
  • Patients' privacy protection is a heated issue in medical business, as medical information in digital format transmit everywhere through networks without any limitation. A current protection method for brain images is to deface from the brain image for patient's privacy. However, the defacing process often removes important brain voxels so that the defaced brain image is damaged for medical analysis. An ad-hoc method is proposed to conceal patient's identification by adding cylindrical mask, while the brain keep all important brain voxels. The proposed lossless deformation of brain image is verified not to loose any important voxels. Futhermore, the masked brain image is proved not to be recognized by others.

EEIRI: Efficient Encrypted Image Retrieval in IoT-Cloud

  • Abduljabbar, Zaid Ameen;Ibrahim, Ayad;Hussain, Mohammed Abdulridha;Hussien, Zaid Alaa;Al Sibahee, Mustafa A.;Lu, Songfeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.11
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    • pp.5692-5716
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    • 2019
  • One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

Analysis of privacy issues and countermeasures in neural network learning (신경망 학습에서 프라이버시 이슈 및 대응방법 분석)

  • Hong, Eun-Ju;Lee, Su-Jin;Hong, Do-won;Seo, Chang-Ho
    • Journal of Digital Convergence
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    • v.17 no.7
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    • pp.285-292
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    • 2019
  • With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

Intelligent Video Surveillance Incubating Security Mechanism in Open Cloud Environments (개방형 클라우드 환경의 지능형 영상감시 인큐베이팅 보안 메커니즘 구조)

  • Kim, Jinsu;Park, Namje
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.5
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    • pp.105-116
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    • 2019
  • Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

A Study on the Video Privacy Protective Mechanism (영상 프라이버시 보호 메커니즘에 관한 연구)

  • Kim, Minsu;Kim, Jongmin;Kim, Sang-Choon
    • Convergence Security Journal
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    • v.17 no.5
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    • pp.49-55
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    • 2017
  • In case of security of IoT-based areas in ICBM industry, the anxiety about safety goes to extremes in public and national safety area, so that the demand for security service related to disaster/safety management is increasing. Like this, as a security service for safety, CCTVs are installed/operated for the purpose of maintenance of public order and crime prevention. Especially, as the recorded images are presented as crucial evidences of crimes, they are rapidly increasing. However, as adverse effects of CCTVs, it is highly possible to unintentionally leak personal information in the process of performing the original purpose, or to violate someone's privacy in case when such technologies are abused. Therefore, it would be necessary to have researches on the multilaterally-combined mechanism for the protection of image privacy.

Similarity measurement based on Min-Hash for Preserving Privacy

  • Cha, Hyun-Jong;Yang, Ho-Kyung;Song, You-Jin
    • International Journal of Advanced Culture Technology
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    • v.10 no.2
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    • pp.240-245
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    • 2022
  • Because of the importance of the information, encryption algorithms are heavily used. Raw data is encrypted and secure, but problems arise when the key for decryption is exposed. In particular, large-scale Internet sites such as Facebook and Amazon suffer serious damage when user data is exposed. Recently, research into a new fourth-generation encryption technology that can protect user-related data without the use of a key required for encryption is attracting attention. Also, data clustering technology using encryption is attracting attention. In this paper, we try to reduce key exposure by using homomorphic encryption. In addition, we want to maintain privacy through similarity measurement. Additionally, holistic similarity measurements are time-consuming and expensive as the data size and scope increases. Therefore, Min-Hash has been studied to efficiently estimate the similarity between two signatures Methods of measuring similarity that have been studied in the past are time-consuming and expensive as the size and area of data increases. However, Min-Hash allowed us to efficiently infer the similarity between the two sets. Min-Hash is widely used for anti-plagiarism, graph and image analysis, and genetic analysis. Therefore, this paper reports privacy using homomorphic encryption and presents a model for efficient similarity measurement using Min-Hash.

De-Identified Face Image Generation within Face Verification for Privacy Protection (프라이버시 보호를 위한 얼굴 인증이 가능한 비식별화 얼굴 이미지 생성 연구)

  • Jung-jae Lee;Hyun-sik Na;To-min Ok;Dae-seon Choi
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.33 no.2
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    • pp.201-210
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    • 2023
  • Deep learning-based face verificattion model show high performance and are used in many fields, but there is a possibility the user's face image may be leaked in the process of inputting the face image to the model. Althoughde-identification technology exists as a method for minimizing the exposure of face features, there is a problemin that verification performance decreases when the existing technology is applied. In this paper, after combining the face features of other person, a de-identified face image is created through StyleGAN. In addition, we propose a method of optimizingthe combining ratio of features according to the face verification model using HopSkipJumpAttack. We visualize the images generated by the proposed method to check the de-identification performance, and evaluate the ability to maintain the performance of the face verification model through experiments. That is, face verification can be performed using the de-identified image generated through the proposed method, and leakage of face personal information can be prevented.

Multi-attribute Face Editing using Facial Masks (얼굴 마스크 정보를 활용한 다중 속성 얼굴 편집)

  • Ambardi, Laudwika;Park, In Kyu;Hong, Sungeun
    • Journal of Broadcast Engineering
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    • v.27 no.5
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    • pp.619-628
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    • 2022
  • Although face recognition and face generation have been growing in popularity, the privacy issues of using facial images in the wild have been a concurrent topic. In this paper, we propose a face editing network that can reduce privacy issues by generating face images with various properties from a small number of real face images and facial mask information. Unlike the existing methods of learning face attributes using a lot of real face images, the proposed method generates new facial images using a facial segmentation mask and texture images from five parts as styles. The images are then trained with our network to learn the styles and locations of each reference image. Once the proposed framework is trained, we can generate various face images using only a small number of real face images and segmentation information. In our extensive experiments, we show that the proposed method can not only generate new faces, but also localize facial attribute editing, despite using very few real face images.

Automatic Detection of Work Distraction with Deep Learning Technique for Remote Management of Telecommuting

  • Lee, Wan Yeon
    • International journal of advanced smart convergence
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    • v.10 no.1
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    • pp.82-88
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    • 2021
  • In this paper, we propose an automatic detection scheme of work distraction for remote management of telecommuting. The proposed scheme periodically captures two consequent computer screens and generates the difference image of these two captured images. The scheme applies the difference image to our deep learning model and makes a decision of abnormal patterns in the difference image. Our deep learning model is designed with the transfer learning technique of VGG16 deep learning. When the scheme detects an abnormal pattern in the difference image, it hides all texts in the difference images to protect disclosure of privacy-related information. Evaluation shows that the proposed scheme provides about 96% detection accuracy.