Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Melzi, Pietro; Shahreza, Hatef Otroshi; Rathgeb, Christian; Tolosana, Rubén; Vera-Rodríguez, Rubén; Fiérrez, Julián; Marcel, Sébastien; Busch, Christoph

doi:10.1109/wacvw58289.2023.00036

Cited by 16 publications

(24 citation statements)

References 19 publications

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“…3) Irreversibility: Although many information-theoretic metrics have been proposed in the literature, a general framework for the evaluation of irreversibility is currently missing [25]. One of the most popular metrics for irreversibility is mutual information (MI).…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Benchmarking of Cancelable Biometrics for Deep Templates

et al. 2022

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Section: B Evaluation Metricsmentioning

confidence: 99%

Benchmarking of Cancelable Biometrics for Deep Templates

et al. 2022

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“…In addition to this, we also evaluate the performance of ChatGPT when considering popular challenges in face recognition such as demographic bias, age and pose variations, and occlusions. The following databases are considered in the evaluation, which are also considered in the recent FRCSyn Challenge [21]:…”

Section: Experimental Framework a Experimental Protocolmentioning

confidence: 99%

“…1 provides a graphical representation of the analysis carried out in this study, focused on the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. Face biometrics is a very challenging task in the field of computer vision and image understanding due to the large intra-user variability produced by factors such as pose, age, illumination, expression, etc., [20], [21].…”

Section: Introductionmentioning

confidence: 99%

How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

Deandres-Tame,

Tolosana,

Vera-Rodriguez

et al. 2024

IEEE Access

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Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the release of ChatGPT which allows anyone to interact in a simple conversational way with LLMs, without any experience in the field needed. As a result, ChatGPT has been rapidly applied to many different tasks such as code-and song-writer, education, virtual assistants, etc., showing impressive results for tasks for which it was not trained (zero-shot learning). The present study aims to explore the ability of ChatGPT, based on the recent GPT-4 multimodal LLM, for the task of face biometrics. In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. ChatGPT could be very valuable to further increase the explainability and transparency of the automatic decisions in human scenarios. Experiments are carried out in order to evaluate the performance and robustness of ChatGPT, using popular public benchmarks and comparing the results with state-of-the-art methods in the field. The results achieved in this study show the potential of LLMs such as ChatGPT for face biometrics, especially to enhance explainability. For reproducibility reasons, we release all the code in GitHub.

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“…These variables were then incrementally removed from the facial templates to suppress gender and age features while maintaining high face-matching performance. In [16] Melzi et. al.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics

Chouchane,

Panariello,

Zari

et al. 2023

Proceedings of the 2023 ACM Workshop on Information Hiding and Multimedia Security

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Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become increasingly widespread in response to the growing need for secure and efficient identity verification methods. The voice data encompasses a wealth of personal information, which includes but is not limited to gender, age, health condition, stress levels, and geographical and socio-cultural origins. These attributes, known as soft biometrics, are private and the user may wish to keep them confidential. However, with the advancement of machine learning algorithms, soft biometrics can be inferred automatically, creating the potential for unauthorized use. As such, it is crucial to ensure the protection of these personal data that are inherent within the voice while retaining the utility of identity recognition. In this paper, we present an adversarial Auto-Encoder-based approach to hide gender-related information in speaker embeddings, while preserving their effectiveness for speaker verification. We use an adversarial procedure against a gender classifier and incorporate a layer based on the Laplace mechanism into the Auto-Encoder architecture. This layer adds Laplace noise for more robust gender concealment and ensures differential privacy guarantees during inference for the output speaker embeddings. Experiments conducted on the VoxCeleb dataset demonstrate that speaker verification tasks can be effectively carried out while concealing speaker gender and ensuring differential privacy guarantees; moreover, the intensity of the Laplace noise can be tuned to select the desired trade-off between privacy and utility.

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Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Cited by 16 publications

References 19 publications

Benchmarking of Cancelable Biometrics for Deep Templates

Benchmarking of Cancelable Biometrics for Deep Templates

How Good Is ChatGPT at Face Biometrics? A First Look Into Recognition, Soft Biometrics, and Explainability

Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics

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