ONOT/TONO-Print: A Paired Benchmark for Identity-Document Portrait Quality under Document Production Shift

Alexander Vinogradov

cs.CVOct 24, 2026

ONOT/TONO-Print: A Paired Benchmark for Identity-Document Portrait Quality under Document Production Shift

Authors: Alexander Vinogradov

Organizations: IU International University of Applied Sciences

Abstract

Identity-document portraits are modified during document production by printing, engraving, and integration into the document template. These transformations may change image texture, contrast, colour, and other visual properties without necessarily changing the underlying biometric or presentation information. In this work, we introduce ONOT/TONO-Print, a small paired benchmark for evaluating face-image quality models under identity-document production shift. The benchmark is based on ONOT and TONO portraits and contains synthetic variants representing five common production techniques: toner, inkjet, dye sublimation, laser engraving, and thermal transfer. A smaller physical subset contains portraits printed and scanned using inkjet and toner devices. The benchmark is used to compare the behaviour of biometric and ICAO-related face-quality components before and after production. Results show that production can introduce substantial domain shift for some quality estimators, while other models remain comparatively stable.

Image Quality Assessment

Image Quality Assessment measures perceived image quality, distortion, and usefulness with computational metrics and human-aligned evaluation.

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Oct 26, 2026J/K move · Enter open · S save

Printed and Scanned: Exposing the Fragility of Single-Image Morphing Attack Detectors under Print-Scan Degradation

Diogo J. Paulo, Hugo Proença, João C. Neves

University of Beira Interior · NOVA LINCS · IT: Instituto de Telecomunicac¸˜oes

Image Forgery Detection Face Presentation Attack Detection Image Corruption Robustness Face Anti-Spoofing

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Face morphing attacks, in which the biometric traits of two or more identities are blended into a single face image, pose a severe threat to identity document issuance and automated border control. Consequently, a large body of work has proposed Single-image Morphing Attack Detection (S-MAD) algorithms and benchmarked them on datasets such as SYN-MAD22. However, the majority of these benchmarks operate exclusively in the digital domain, which contrasts with the real-world passport issuance pipeline in some countries, which still requires an applicant-supplied photograph to be printed and subsequently re-scanned before it is embedded in the identity document or fed into an automated verification system. This print-scan step introduces halftoning, resampling, color-space, and sensor noise that are absent from digital pipelines, and may mask the high-frequency artifacts that S-MAD models rely on. In this work, we introduce a print-scan version of the SYN-MAD22 dataset, obtained by printing and re-scanning all bona fide and morphed images at 300 and 600 DPI, and we use it to evaluate eight state-of-the-art S-MAD models trained exclusively on digital data. We additionally repeat the acquisition with a second scanner to assess cross-device robustness. Our results show that detectors that achieve near state-of-the-art performance in the digital domain suffer a more than threefold increase in average equal error rate, with the same qualitative degradation observed across both scanning devices, exposing a critical, previously under-reported gap between digital-benchmark performance and robustness to print-scan degradation.

Abstract

Image Quality Assessment

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