Face Detection and Encryption for Privacy Preserving in Surveillance Video

Liu, Suolan; Kong, Lingbao; Wang, Hongyuan

doi:10.1007/978-3-030-03338-5_14

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The last subgroup of obfuscation-based B-PETs relies on various image transformations to conceal (remove or obscure) sensitive regions in facial images or video. These transformations include image subsampling [135], scrambling [137], [140], [154], [155], [163], mosaicing [110], [162], warping [143], morphing [145], foveation [153], halftoning [161], image puzzling [149], steganography, and others. Techniques from this group are often tied to various compression standards [147] and exploit selected characteristics of the standards for privacy enhancement.…”

Section: Image Transformationsmentioning

confidence: 99%

“…Liu et al [163] introduced an approach to privacy enhancement in video that relies on skin color information to detect face-region candidates and an SVM-based classifier to refine candidate regions and determine final face locations. (Reversible) scrambling procedures are utilized to modify both spatial positions of pixels within a face region as well as their values.…”

Section: Image Transformationsmentioning

confidence: 99%

See 1 more Smart Citation

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

104

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Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

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Section: Image Transformationsmentioning

confidence: 99%

Section: Image Transformationsmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

104

View full text Add to dashboard Cite

show abstract

“…Additionally, Liu et al [12] proposed an efficient method based on spatial and value scrambling models to preserve face regions on surveillance videos. This scheme was able to localize and encrypt multiple faces well with satisfied antiattack property.…”

Section: Introductionmentioning

confidence: 99%

Efficient NewHope Cryptography Based Facial Security System on a GPU

2020

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With explosive era of machine learning development, human data, such as biometric images, videos, and particularly facial information, have become an essential training resource. The popularity of video surveillance systems and growing use of facial images have increased the risk of leaking personal information. On the other hand, traditional cryptography systems are still expensive, time consuming, and low security, leading to be threatened by the foreseeable attacks of quantum computers. This paper proposes a novel approach to fully protect facial images extracted from videos based on a post-quantum cryptosystem named NewHope cryptography. Applying the proposed technique to arrange input data for encryption and decryption processes significantly reduces encryption and decryption times. The proposed facial security system was successfully accelerated using data-parallel computing model on the recently launched Nvidia GTX 2080Ti Graphics Processing Unit (GPU). Average face frame extracted from video (190 × 190 pixel) required only 2.2 ms and 2.7 ms total encryption and decryption times with security parameters n = 1024 and n = 2048, respectively, which is approximately 9 times faster than previous approaches. Analysis results of security criteria proved that the proposed system offered comparable confidentiality to previous systems. INDEX TERMS cryptosystem, facial security system, graphics processing unit, NewHope, public-key encryption.

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“…The transformation technique extends to reversible watermarking techniques for embedding and later extracting facial regions [102], video anonymization through pixel relocation and semi-blind watermarking by Cichowski and Czyzewski [33], and Liu et al's reversible scrambling based on 3. Related Works skin color detection and Support Vector Machine (SVM) classifiers for action recognition in videos [126]. Techniques like the chaos cryptography-based data scrambling for surveillance video by Rahman et al [159] and Melle and Dugelay's reversible scrambling using image self-similarities [135] demonstrate the diverse applications and the balance between privacy protection and data intelligibility.…”

mentioning

confidence: 99%

Beyond Recognition : Privacy Protections in a Surveilled World

Minh-Ha

2024

Linköping Studies in Science and Technology. Dissertations

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Våra ansikten är unika och är en integrerad del av vår identitet. Med smartphones och sociala medier finns det miljarder ansiktsbilder online varje dag. Denna data driver ansiktsigenkänningsteknik, som används för allt från att låsa upp telefoner till omfattande övervakning. I takt med att ansiktsigenkänningstekniken utvecklas, ökar också dess förmåga att identifiera oss -ofta utan vårt samtycke. Denna verklighet leder oss till en avgörande fråga: Hur kan vi skydda vår integritet utan att offra fördelarna med den digitala åldern?Vår forskning antar denna utmaning genom att undersöka hur ansiktsigenkänningsteknik fungerar, dess fördelar för säkerhet och bekvämlighet, samt de integritetsrisker den medför. Vi har utvecklat innovativa strategier för att skydda vår ansiktsdata från icke-samtyckt övervakning samtidigt som vi behåller nyttan med systemen inom legitima områden som behöver anonymiserade data för samhällets bästa, såsom sjukvård, självkörande fordon och maskininlärning. För att navigera dessa utmaningar har vi introducerat banbrytande verktyg och metoder för anonymisering, såsom AnonFACES, Sty-leID och utforskningen av sårbarheter i FRS genom IdDecoder.Föreställ dig en värld där du kan dela bilder online utan rädsla för att de utnyttjas av övervakningssystem. För att ta itu med dessa problem har vi skapat nya verktyg och tekniker, såsom DiffPrivate och StyleAdv. Dessa innovationer gör ansiktsbilder "osynliga" för övervakningssystem utan att ändra hur de ser ut för mänskliga ögon. Dessa verktyg är konstruerade för att vara offentligt tillgängliga samtidigt som de ger oss möjligheten att effektivt skydda vår integritet.Vårt arbete banar väg för en framtid där tekniken ger oss möjlighet att njuta av de stora möjligheterna med den digitala världen samtidigt som vi upprätthåller vår grundläggande rätt till integritet. Det handlar om att upprätthålla en harmonisk balans mellan innovation och integritet, och säkerställa att våra unika identiteter, som representeras av våra ansikten, förblir uteslutande våra i en värld som ständigt söker märka och kategorisera oss.

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Face Detection and Encryption for Privacy Preserving in Surveillance Video

Cited by 7 publications

References 15 publications

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Efficient NewHope Cryptography Based Facial Security System on a GPU

Beyond Recognition : Privacy Protections in a Surveilled World

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