Bad Ai: Investigating the Effect of Half-Toning Techniques on Unwanted Face Detection Systems

Dadkhah, Sajjad; Koeppen, Mario; Sadeghi, Somayeh; Yoshida, Kaori

doi:10.1109/ntms.2018.8328726

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…The initial privacy-preserving methods that were put forth relied on hiding the individual's face. This indicates that various techniques, such as masking [11]- [13], filtering [14]- [16], and transformation [17]- [19], eliminate personally identifiable information. The face region is covered with a shape in the masking approach so that the person's face is fully hidden; filtering and transformation reduce the face region's resolution; and blurring employs Gaussian filters with different standard deviation values to allow for varying degrees of blurring.…”

Section: B Face Obfuscationmentioning

confidence: 99%

“…The original face image is rendered unrecognizable through quality reduction in traditional face privacy methods. They remove any information that might reveal someone's identity by using masking [11]- [13], filtering [14]- [16], and transformation [17]- [19]. Using facial details for specific applications or deriving meaningful insights is difficult when using traditional methods because they deteriorate the overall quality of the images.…”

Section: Introductionmentioning

confidence: 99%

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Face De-Identification Using Face Caricature

Laishram,

Lee,

Jung

2024

IEEE Access

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Face privacy concerns revolve around the ethical, social, and technological implications of collecting, storing, and using facial data. With the advancement of deep learning techniques, realistic face privacy involves techniques that obscure or alter facial features effectively without compromising the usability or quality of the visual content. Modern face privacy techniques suffer from three main problems:(1) lack of human perception, (2) indistinguishability, and (3) loss of facial attributes. Modern face privacy techniques generate random, realistic faces to conceal the identifiable features of the original faces but lack the application of human perception to face de-identification. Indistinguishability arises with the highly realistic nature of fake faces used in face privacy, making it difficult to distinguish whether a face has been manipulated or not. Most face-privacy methods fail to retain the facial attributes of the de-identified faces. Our face de-identification method is designed to address all three issues mentioned. We propose a novel face de-identification method that considers both human perception and face recognition models when de-identifying a face. We explore the tradeoff between a user misidentifying the original identity with a well-known celebrity and a facial recognition model that tries to identify the original identity. We generate caricature faces of the de-identified faces to ensure our manipulated faces can be distinguished effortlessly. The face caricatures are the exaggeration of the eyes and mouth region, and we provide different exaggeration scales depending on preference and application. We perform an attribute preservation optimization process to retrieve all the facial attributes. We demonstrate our method through a series of both qualitative and quantitative experiments with numerous user studies.

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Section: B Face Obfuscationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face De-Identification Using Face Caricature

Laishram,

Lee,

Jung

2024

IEEE Access

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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%

“…Dadkhah et al [161] studied possibilities for applying different half-toning algorithms with the goal of avoiding unwanted (automated) face detection and recognition. Halftoning transforms the standard grey-level pixel intensities of the input images into black and white dots in a way that preserves the intelligibility of images for human observes, TABLE 3: High-level comparison of the surveyed obfuscation privacy-enhancing techniques.…”

Section: Image Transformationsmentioning

confidence: 99%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

124

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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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“…PrivacyCam by Chattopadhyay and Boult [15] encrypted quantized DCT coefficients for reversible privacy enhancement, while Kobayashi et al [33] combined reversible mosaicing and watermarking in the Privacy Protection Surveillance Camera System. Other approaches include Korshunov and Ebrahimi's use of warping and morphing to reduce biometric utility [105], Ruchaud and Dugelay's combination of steganography and scrambling [165], Chriskos et al's face-detection hindering technique [32], and Dadkhah et al's exploration of de-identification algorithm called half-toning to impede automated face detection and recognition [41].…”

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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Bad Ai: Investigating the Effect of Half-Toning Techniques on Unwanted Face Detection Systems

Cited by 5 publications

References 15 publications

Face De-Identification Using Face Caricature

Face De-Identification Using Face Caricature

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Beyond Recognition : Privacy Protections in a Surveilled World

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