CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with similar cases. However, case-based explanations cannot be used in contexts where the data exposes personal identity, as they may compromise the privacy of individuals. In this work, we identify the main limitations and challenges in the anonymization of casebased explanations of image data through a survey on case-based interpretability and image anonymization methods. We empirically analyze the anonymization methods in regards to their capacity to remove personally identifiable information while preserving relevant semantic properties of the data. Through this analysis, we conclude that most privacy-preserving methods are not sufficiently good to be applied to casebased explanations. To promote research on this topic, we formalize the privacy protection of visual casebased explanations as a multi-objective problem to preserve privacy, intelligibility, and relevant explanatory evidence regarding a predictive task. We empirically verify the potential of interpretability saliency maps as qualitative evaluation tools for anonymization. Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.

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“…One example of such a method is CLEANIR [34]. CLEANIR is a Variational Autoencoder (VAE) applied to de-identify face images.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

et al. 2022

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“…Generative face anonymization After the advent of GANs [14], several lines of work have been proposed to tackle the problem of anonymization leveraging the generative power of these networks. Prior to this, [6,27] proposed auto-encoder based methods, in particular Cho et al [6] used such networks to learn to disentangle the identity information from the attributes given a vector representation of an image, allowing then to tweak the identity part of the vector to obtain an anonymized subject. This work reported emotion preservation results, however in this case we are concerned with the preservation of facial attributes more generally.…”

Section: Related Workmentioning

confidence: 99%

Attribute-preserving Face Dataset Anonymization via Latent Code Optimization

Barattin¹,

Tzelepis²,

Patras³

et al. 2023

Preprint

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This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dataset is useful for downstream task such as for training machine learning models. To the best of our knowledge, we are the first to explicitly address this issue and deal with two major drawbacks of the existing state-of-the-art approaches, namely that they (i) require the costly training of additional, purpose-trained neural networks, and/or (ii) fail to retain the facial attributes of the original images in the anonymized counterparts, the preservation of which is of paramount importance for their use in downstream tasks. We accordingly present a task-agnostic anonymization procedure that directly optimizes the images' latent representation in the latent space of a pre-trained GAN. By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's [49] deep feature space). We demonstrate through a series of both qualitative and quantitative experiments that our method is capable of anonymizing the identity of the images whilstcrucially-better-preserving the facial attributes. We make the code and the pre-trained models publicly available at: https://github.com/chi0tzp/FALCO.

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“…Next to GANs, other generative deep models have also been considered when developing B-PETs for facial images. In [230], for instance, Cho et al described CLEANIR, a Variational Auto-Encoder (VAE) [255] based face deidentification technique. With CLEANIR, a disentangled representation (separating identity from other attributes) is learned in the VAE latent space.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

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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CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

Cited by 20 publications

References 40 publications

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

Attribute-preserving Face Dataset Anonymization via Latent Code Optimization

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

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