Face Spoofing Detection using Deep Learning

• URL: http://arxiv.org/abs/2503.19223v2
• Date: Fri, 31 Oct 2025 00:26:19 GMT
• Title: Face Spoofing Detection using Deep Learning
• Abstract summary: Digital image spoofing has emerged as a significant security threat in biometric authentication systems.<n>This study evaluates the performance of three vision based models, MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in image classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. This study evaluates the performance of three vision based models, MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in image classification, utilizing a dataset of 150,986 images divided into training , 140,002, testing, 10,984, and validation ,39,574, sets. Spoof detection is critical for enhancing the security of image recognition systems, and this research compares the models effectiveness through accuracy, precision, recall, and F1 score metrics. Results reveal that MobileNetV2 outperforms other architectures on the test dataset, achieving an accuracy of 91.59%, precision of 91.72%, recall of 91.59%, and F1 score of 91.58%, compared to ViT 86.54%, 88.28%, 86.54%, and 86.39%, respectively. On the validation dataset, MobileNetV2, and ViT excel, with MobileNetV2 slightly ahead at 97.17% accuracy versus ViT 96.36%. MobileNetV2 demonstrates faster convergence during training and superior generalization to unseen data, despite both models showing signs of overfitting. These findings highlight MobileNetV2 balanced performance and robustness, making it the preferred choice for spoof detection applications where reliability on new data is essential. The study underscores the importance of model selection in security sensitive contexts and suggests MobileNetV2 as a practical solution for real world deployment.

Related papers

• ForensicNet: Lightweight Attention-Enhanced MobileNetV2 for Automated Face Identification [0.0]
  This paper presents a lightweight deep learning framework for forensic face recognition that enhances attention.<n>The suggested model combines the MobileNetV2 backbone with Convolutional Block Attention Modules (CBAM) to improve the learning of discriminative features.<n>The framework requires only 2.1 GFLOPs per inference, and hence can be used in real-time forensic surveillance applications.
  arXiv  Detail & Related papers  (2026-07-08T09:07:52Z)
• On the Study of Biometric Spoofing Detection using Deep Learning [0.0]
  This research evaluates the effectiveness of machine learning models, MobileNetV2, DenseNet-121, Inception-v3, and Spoof Trace Disentanglement (STD) in detecting spoofing attacks within facial recognition systems.<n>The results show MobileNetV2 as the most efficient model, achieving 92% accuracy while balancing computational effectiveness, making it appropriate for real-life applications.
  arXiv  Detail & Related papers  (2026-06-09T22:56:43Z)
• Understanding vision transformer robustness through the lens of out-of-distribution detection [59.72757235382676]
  Quantization reduces memory and inference costs at the risk of performance loss.<n>We investigate the behaviour of quantized small-variant popular vision transformers (DeiT, DeiT3, and ViT) on common out-of-distribution (OOD) datasets.
  arXiv  Detail & Related papers  (2026-02-01T22:00:59Z)
• Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns [0.0]
  This study evaluates the efficacy of three deep learning architectures: ResNet50, MobileNetV2, and EfficientNetB0 for automated plant species classification based on leaf venation patterns.
  arXiv  Detail & Related papers  (2025-09-03T21:23:09Z)
• Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and CNN from Scratch [0.0]
  This research proposes one of the supervised learning methods, CNN.<n>Using MobileNetV2 as the pre-trained one with ResNet101V2 architecture and using Keras API as the built from scratch model, for identifying lung diseases especially pneumonia.
  arXiv  Detail & Related papers  (2025-05-05T06:40:08Z)
• Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques [1.024113475677323]
  This study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains.<n>To complete the classification job, features are extracted from input photos using a Convolutional Neural Network (CNN) architecture.<n>Our study shows that transfer learning models and MobileNetV2 in particular, work well for landscape categorization.
  arXiv  Detail & Related papers  (2025-03-04T11:19:18Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing [0.0]
  Face recognition systems are vulnerable to spoofing attacks, where attackers use photos, videos, or masks to impersonate legitimate users. This research addresses these vulnerabilities by exploring the Vision Transformer (ViT) architecture, fine-tuned with the DINO framework.
  arXiv  Detail & Related papers  (2024-06-19T21:44:43Z)
• Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches [0.51795041186793]
  This study explores the effectiveness of machine learning approaches, specifically transfer learning (TL) models, for defect detection in 3D-printed cylinders. Images of cylinders were analyzed using models including VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2. Results suggest certain TL models can deliver high accuracy for AM defect classification, although performance varies across algorithms.
  arXiv  Detail & Related papers  (2023-10-12T18:10:36Z)
• Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation [1.3701366534590498]
  Ocular Toxoplasmosis (OT) is a common eye infection caused by T. gondii that can cause vision problems. This research seeks to provide a guide for future researchers looking to utilise DL techniques and develop a cheap, automated, easy-to-use, and accurate diagnostic method.
  arXiv  Detail & Related papers  (2023-05-18T13:42:15Z)
• ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection [36.05807963935458]
  We leverage a vision transformer architecture for joint spoof detection and matching. We report competitive results with state-of-the-art (SOTA) models for both a sequential system and a unified architecture. We demonstrate the capability of our unified model to achieve an average integrated matching (IM) accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors.
  arXiv  Detail & Related papers  (2023-05-12T16:51:14Z)
• Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification [71.06194656633447]
  We establish an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set. UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images.
  arXiv  Detail & Related papers  (2023-04-08T10:47:41Z)
• Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement [68.44100784364987]
  We propose a strategy to improve a dataset once such that the accuracy of any model architecture trained on the reinforced dataset is improved at no additional training cost for users. We create a reinforced version of the ImageNet training dataset, called ImageNet+, as well as reinforced datasets CIFAR-100+, Flowers-102+, and Food-101+. Models trained with ImageNet+ are more accurate, robust, and calibrated, and transfer well to downstream tasks.
  arXiv  Detail & Related papers  (2023-03-15T23:10:17Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• VOLO: Vision Outlooker for Visual Recognition [148.12522298731807]
  Vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification. We introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO) Unlike self-attention that focuses on global dependency modeling at a coarse level, the outlook attention efficiently encodes finer-level features and contexts into tokens. Experiments show that our VOLO achieves 87.1% top-1 accuracy on ImageNet-1K classification, which is the first model exceeding 87% accuracy on this competitive benchmark.
  arXiv  Detail & Related papers  (2021-06-24T15:46:54Z)
• Vision Transformers are Robust Learners [65.91359312429147]
  We study the robustness of the Vision Transformer (ViT) against common corruptions and perturbations, distribution shifts, and natural adversarial examples. We present analyses that provide both quantitative and qualitative indications to explain why ViTs are indeed more robust learners.
  arXiv  Detail & Related papers  (2021-05-17T02:39:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.