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Yoannita
Multi Data Palembang University
Computer Science & IT

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Implementation of MobileNetV4 and Efficient Channel Attention in Anti-Spoofing Face Attack Detection Rayvin Suhartoyo; Yoannita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/kth2nc32

Abstract

Face Anti-Spoofing (FAS) is essential for preventing presentation attacks in biometric systems, yet deploying robust models on mobile devices remains a challenge due to computational constraints. This study proposes a lightweight FAS model integrating the MobileNetV4 architecture with an Efficient Channel Attention (ECA) module. The ECA mechanism is designed to enhance the network’s ability to detect subtle spoofing artifacts, such as texture anomalies, with negligible computational overhead. The model was evaluated using a dataset of 6,400 images, comprising both bona fide and attack presentations. Experimental results demonstrate robust performance, achieving an overall accuracy of 99.69%, 100% precision, and an Average Classification Error Rate (ACER) of 0.25%. Crucially, the model yielded a Bona Fide Presentation Classification Error Rate (BPCER) of 0.00%, ensuring that no genuine users are falsely rejected. While the baseline architecture provided a strong benchmark, the proposed attention-enhanced framework offers a viable trade-off between security and usability, providing a computationally efficient solution suitable for real-time mobile authentication.
Implementation of YOLO26 for Mold Detection on White Bread Based on Digital Imagery Malvin Hendrawan; Yoannita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ybbmhr95

Abstract

White bread is highly susceptible to visible mold contamination, which causes physical deterioration and potential health risks. Conventional manual visual inspection is slow, subjective, and inconsistent, necessitating an automated detection system. This study implemented the YOLO26n algorithm for mold contamination detection on white bread based on digital imagery. A primary dataset of 300 images (150 fresh bread and 150 moldy breads) was collected independently, annotated via Roboflow, and split into 70% training, 20% validation, and 10% testing. The model was trained on Google Colab using the MuSGD optimizer with 200 epochs. The YOLO26n model achieved an overall precision of 0.827, recall of 0.734, and mAP50 of 0.711, with an inference speed of 8.1 ms per image, demonstrating its potential as a fast and lightweight solution for automated mold inspection, though further improvement in moldy bread detection performance is required before reliable deployment in bakery production lines.
Co-Authors Malvin Hendrawan Rayvin Suhartoyo