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SignatureGuard is a three-task framework that evaluates six hybrid CNN\u2013transformer architectures on two offline signature benchmarks (one Arabic, ASVAR; one English, CEDAR) under a single shared preprocessing and training pipeline, enabling direct architectural comparison across writing systems. The three tasks are binary forgery detection, multi-class biometric identification, and forgery source identification. To address the Arabic data gap, we publicly released ASVAR: 3471 images (1712 genuine, 1759 forged) from 70 individuals. Hybrid pairings of EfficientNetB7 or ResNet50 with the Vision Transformer (ViT-B\/16) achieve test accuracies of 98.2% and 98.4% on forgery detection, macro-F1 above 0.97, and Cohen\u2019s\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\kappa$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    above 0.96; 95% Wilson confidence intervals (\n                    <jats:inline-formula>\n                      <jats:tex-math>$${\\pm }$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    1.5\u00a0pp) confirm these are not artefacts of finite test-set size. MobileNetV2-based hybrids trail by at most 0.7 percentage points. Architectural rankings are broadly consistent across both datasets. All results are obtained under a seen-writer, image-level 80\/10\/10 split\u2014a closed-set protocol that supports reproducible architectural comparison but overestimates real-world deployment performance; a signer-disjoint evaluation is identified as the primary follow-on experiment. An information-theoretic argument demonstrates that the hybrid classification head cannot perform worse than either frozen component in isolation. Confusion-matrix analysis, multi-seed validation, and backbone fine-tuning are recommended as extensions.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-62860-1","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:56:26Z","timestamp":1785250586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SignatureGuard: hybrid CNN\u2013transformer model for signature verification and identification across Arabic and English datasets"],"prefix":"10.1038","volume":"16","author":[{"given":"Mazen","family":"Balat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esraa","family":"Mamdouh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rewaa","family":"Awaad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Elhoseny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed M.","family":"Anter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"62860_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","volume":"14","author":"A Jain","year":"2004","unstructured":"Jain, A., Ross, A. & Prabhakar, S. 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Formal institutional review board approval was not required, as participation was voluntary, and no medical, personal, or private data were collected. The dataset consists solely of signature images and contains no personally identifying information. Dataset is publicly available online in anonymized form. Data Collection Committee, Professor Hossam Kasem, E-JUST University, Professor Ahmed Anter, E-JUST University, Professor Ahmed Fares, E-JUST University.","order":2,"name":"Ethics","label":"Human and animal participants","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"It was obtained from all participants prior to their involvement in the study. All contributors received a description of the study\u2019s purpose, the data to be collected (signature images only), and the plan to release the dataset publicly in anonymized form. Participation was voluntary, and contributors could withdraw at any point without giving a reason. No personally identifying information is linked to any image in the dataset.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"23549"}}