https://arxiv.org/api/3wrULcYuJS6XMLXjZEsTjmUnTeo 2026-09-25T16:59:29Z 12 4 0 http://arxiv.org/abs/2609.13598v1 2026-09-11T23:08:50Z

Age verification is often treated as a technical problem: can a system determine a child's age accurately? We argue this framing is too narrow. Age assurance becomes consequential when evidence is evaluated, translated into age-related claims, and used to decide whether a person can access, purchase, or belong. We review 85 publications on children's age assurance published from 2020 through February 2026. We find that shared terms such as age verification describe different processes. Age is represented as threshold eligibility or inferred estimation, and the same eligibility claim can arise from different evidence and components. Rights, access, and privacy receive more attention than accuracy, error, and fairness; yet institutional actors are rarely connected to system failures or user remedies, an accountability gap. We introduce the Age-Assurance Process Framework, which treats age assurance as a sociotechnical process connecting evidence, evaluation, claims, and decisions rather than reducing it to a technical problem.

2026-09-11T23:08:50Z Renkai Ma Prakriti Dumaru Thomas Synaepa-Addison Jess Kropczynski Pamela J. Wisniewski http://arxiv.org/abs/2608.21009v1 2026-08-21T11:54:32Z

Ensuring that Extended Reality (XR) environments are age-appropriate is an important regulatory and safety challenge. However, current age assurance operates only at registration and cannot verify the age of the active user during a session. Face-based approaches, the dominant solution in social media and adult platforms, are impractical in XR, because they require removing the headset and taking a self-captured image, often on a mobile app. This both breaks immersion and introduces the privacy risk of sharing face pictures with third parties, which leaves XR platforms without a viable path to continuous, in-session and privacy-preserving age assurance. We propose the dorsal part of the hand as an alternative to the face, by exploiting the egocentric cameras that XR headsets inherently and naturally use to capture gesture interactions. To evaluate this, we collect an age- and sex-stratified, ethnodiverse dataset of 436 participants spanning the minor--adult boundary, captured under unconstrained lighting and orientation conditions. To characterise what is achievable with off-the-shelf methods at the minor--adult boundary, we evaluate standard neural network architectures for age assurance at the legally critical 18-year threshold. Analysis confirms performance is robust to skin-tone variation. On this dataset, the challenge-31 operating point achieves zero minor admission, making the system a viable first-stage filter for age assurance. These findings position dorsal hand morphometrics as an effective and more privacy-preserving biometric modality for in-session age assurance in XR.

2026-08-21T11:54:32Z Riccardo Bovo George Loukas Josh P. Davis http://arxiv.org/abs/2603.25695v1 2026-03-26T17:42:06Z

In this paper, we provide an overview and evaluation of different types of age assurance technologies (AAT). We describe and analyse 1) different approaches to age assurance online (age verification, age estimation, age inference, and parental control and consent), as well as 2) different age assurance architectures (online, offline device-based, offline credential-based), and assess their various combinations with regards to their respective a) effectiveness, b) side effects, and c) acceptance. We then discuss general limitations of AAT's effectiveness stemming from the possibility of circumvention and outline the most important side effects, in particular regarding privacy and anonymity of all users; bias, discrimination, and exclusion; as well as censorship and related concerns. We conclude our analyses by offering some recommendations on which types of AAT are better or less suited to protect minors online. Guiding our assessment is a weighing of effectiveness against side effects, resulting in a graduated hierarchy of acceptable AAT mechanisms.

2026-03-26T17:42:06Z 53 pages, 1 figure Wouter Lueks Stephan Dreyer Hannes Federrath Judith Simon http://arxiv.org/abs/2508.08808v1 2025-08-12T10:02:42Z

Face aging or de-aging with generative AI has gained significant attention for its applications in such fields like forensics, security, and media. However, most state of the art methods rely on conditional Generative Adversarial Networks (GANs), Diffusion-based models, or Visual Language Models (VLMs) to age or de-age faces based on predefined age categories and conditioning via loss functions, fine-tuning, or text prompts. The reliance on such conditioning leads to complex training requirements, increased data needs, and challenges in generating consistent results. Additionally, identity preservation is rarely taken into accountor evaluated on a single face recognition system without any control or guarantees on whether identity would be preserved in a generated aged/de-aged face. In this paper, we propose to synthesize aged and de-aged faces via editing latent space of StyleGAN2 using a simple support vector modeling of aging/de-aging direction and several feature selection approaches. By using two state-of-the-art face recognition systems, we empirically find the identity preserving subspace within the StyleGAN2 latent space, so that an apparent age of a given face can changed while preserving the identity. We then propose a simple yet practical formula for estimating the limits on aging/de-aging parameters that ensures identity preservation for a given input face. Using our method and estimated parameters we have generated a public dataset of synthetic faces at different ages that can be used for benchmarking cross-age face recognition, age assurance systems, or systems for detection of synthetic images. Our code and dataset are available at the project page https://www.idiap.ch/paper/agesynth/

2025-08-12T10:02:42Z Accepted for publication in IEEE International Joint Conference on Biometrics (IJCB), 2025 Luis S. Luevano Pavel Korshunov Sebastien Marcel 10.1109/IJCB65343.2025.11411093