OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

Ghafourian, Mahdi; Fiérrez, Julián; Vera-Rodríguez, Rubén; Serna, Ignacio; Morales, Aythami

doi:10.1109/wacvw54805.2022.00038

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…The two feature vectors from the face and periocular regions are fused at the feature level by conjunction. Built on time-varying keys obtained from the One-Time Biometrics via morphing (OTB-morph) random face data, the method proposed by Ghafourian et al [98] was executed on a pre-trained Resnet-50 for general image recognition tasks. When applied to face images, OTB-morph can produce artificial faces so that users do not have to expose their real faces, thus improving biometric recognition performance and security.…”

Section: Resnet-50mentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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Biometric recognition is a widely used technology for user authentication. In the application of this technology, biometric security and recognition accuracy are two important issues that should be considered. In terms of biometric security, cancellable biometrics is an effective technique for protecting biometric data. Regarding recognition accuracy, feature representation plays a significant role in the performance and reliability of cancellable biometric systems. How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community, especially from researchers of cancellable biometrics. Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance, while the privacy of biometric data is protected. This survey informs the progress, trend and challenges of feature extraction and learning for cancellable biometrics, thus shedding light on the latest developments and future research of this area.

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Section: Resnet-50mentioning

confidence: 99%

Feature extraction and learning approaches for cancellable biometrics: A survey

Yang,

Wang,

et al. 2024

CAAI Trans on Intel Tech

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“…To address these concerns, adversarial examples have gained attention as a countermeasure. A study by Ghafourian et al [41] evaluates the efficacy of the Basic Iterative Method (BIM) and Iterative Least Likely Class (ILLC), two adversarial methodologies for detecting private face pictures within FR. Fig.…”

Section: Face De-identificationmentioning

confidence: 99%

Preserving Privacy in Video Analytics: A Comprehensive Review of Face De-Identification and Background Blurring Techniques

Maity,

More,

Kambli

et al. 2023

Preprint

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<p>The landscape of privacy protection within the domain of video analytics is in a constant state of evolution, driven by advances in computer vision and machine learning techniques. These advancements provide increasingly sophisticated video analysis capabilities, but they also raise the stakes in terms of personal privacy. In this review, we have conducted a thorough exploration into the intersection of video analytics and privacy preservation, focusing on two core techniques: face de-identification and background blurring. We rigorously analyze the latest advancements in face de-identification, including the nuanced approaches involving facial feature perturbation, deep learning methodologies, and the intriguing domain of generative adversarial networks, meticulously highlighting both their efficacy and inherent limitations. Simultaneously, we research numerous background blurring techniques, showcasing their remarkable ability to obscure contextual information while preserving the essential elements of the visual scene. This paper underscores the practical significance of these techniques through real-world applications in surveillance and the dynamic landscape of social platforms. In essence, our comprehensive review combines the multifaceted aspects of face de-identification and background blurring within the sphere of video analytics, thereby offering a nuanced and thorough understanding of this pivotal domain.</p>

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“…These perturbations are being generated by applying a very slight (imperceptible to human eyes) modification to the input and optimizing it to maximize the probability of misclassifcation by a machine learning classifier (Chakraborty et al 2021;Biggio et al 2013). Using attacks to preserve privacy in biometrics has attracted attention (Ghafourian et al 2022) which also includes adversarial examples. The goal of image cloaking for privacy protection is to suppress the identification rate of the subject while preserving the quality of their images (Hernandez-Ortega et al 2020;Schlett et al 2022) keeping the adversarial perturbation imperceptible.…”

Section: Introductionmentioning

confidence: 99%

Toward Face Biometric De-identification using Adversarial Examples

Ghafourian

Fiérrez

Gomez

et al. 2023

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

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The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media. Recently, researchers turned to use adversarial examples as a countermeasure. In this paper, we assess the effectiveness of using two widely known adversarial methods (BIM and ILLC) for de-identifying personal images. We discovered, unlike previous claims in the literature, that it is not easy to get a high protection success rate (suppressing identification rate) with imperceptible adversarial perturbation to the human visual system. Finally, we found out that the transferability of adversarial examples is highly affected by the training parameters of the network with which they are generated.

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OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

Abstract: https://repositorio.uam.es Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: IEEE Winter Applications and Computer Vision Workshops

Cited by 7 publications

References 48 publications

Feature extraction and learning approaches for cancellable biometrics: A survey

Feature extraction and learning approaches for cancellable biometrics: A survey

Preserving Privacy in Video Analytics: A Comprehensive Review of Face De-Identification and Background Blurring Techniques

Toward Face Biometric De-identification using Adversarial Examples

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