Face Age Verification Vulnerabilities Under Simple Appearance Manipulations

2026-07-27

AI Summary

Current automated facial age verification systems are vulnerable to circumvention by minors using simple appearance modifications, such as drawing beards or applying lipstick. This study presents the first systematic evaluation of the robustness of seven state-of-the-art visual and multimodal models against four types of minimalistic appearance manipulations across three datasets, employing lightweight linear probes to mitigate dataset bias. Experimental results demonstrate that such manipulations can cause up to 61% of genuine negative samples to be misclassified as positive. Moreover, vulnerability exhibits significant demographic disparities: individuals of Indian descent are more susceptible to beard-based spoofing, and females experience higher overall false acceptance rates than males. This work quantifies, for the first time, the performance degradation of age verification models under minimal adversarial appearance changes and reveals socially consequential disparities in their failure modes.

Abstract

Online platforms increasingly rely on automated age estimation systems to enforce minimum-age policies. Focusing on vision-based models designed for this task, concerns arise regarding their robustness to simple appearance changes that underage individuals may use to bypass such systems, such as drawing a mustache or applying lipstick. In this work, we present a systematic study of age verification robustness by simulating visual alterations that can be easily achieved by underage individuals. We evaluate seven models, including vision, vision-language, and multimodal large language models, across three datasets and four manipulation types. Interestingly, under drawn beard stubble, up to 61% of True Negatives are flipped into False Positives. Furthermore, we investigate how different demographics are affected by such manipulations, finding that Indians are more affected by beard stubble manipulations, while females are more affected than males across all manipulations. Finally, we explore how these biases can be mitigated using bias mitigation methodologies in lightweight linear probe settings.

Problem

• age verification
• appearance manipulation
• robustness
• demographic bias
• face recognition

Innovation

• age verification robustness
• appearance manipulation
• demographic bias
• multimodal models
• bias mitigation