EfficientNet-Lite
Parameter-Efficient CNNs for Face Morph Attack Detection
DOI:
https://doi.org/10.14500/aro.12990Keywords:
Biometric security, Convolutional neural networks, EfficientNet, Morph attack detection, Transfer learningAbstract
Morphing attacks on facial images pose a serious threat to face recognition systems during identity verification. In this paper, we introduce a parameter-efficient family of convolutional neural networks, named EfficientNet-Lite (EN_Lite), for morph attack detection (MAD). The experimental framework uses eight datasets, four morph generation methods, and ten homogeneous and heterogeneous evaluation scenarios under a leave-one-datasetout protocol. Performance was assessed using dataset-size-weighted average equal error rate (EERWA ), computed across the held-out datasets within each scenario. EN_Lite was evaluated using ImageNet1K and VGGFace2-HQ pre-training, with the latter comprising 1,107,817 successfully processed facial images. The ImageNetpretrained IEN_Lite-1 achieved a mean EERWA of 2.99%, compared with 2.65% for EfficientNet-B3 while using approximately 76% fewer parameters. It also achieved a lower mean EERWA than GhostNet-1.0x (4.01%), MobileNetV3-Large (5.03%), MobileNetV3-Small (6.03%), and ShuffleNet V2 (5.31%). The VGGFace2-HQ-pretrained variants produced higher error rates than their corresponding ImageNet-pretrained variants under the evaluated protocol. Performance remained strong in homogeneous scenarios but degraded under heterogeneous cross-morph evaluation. Overall, EN_Lite provides a favorable efficiency–performance trade-off for MAD, while high-security deployment should be assessed at application-specific operating points.
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Aghdaie, P., Chaudhary, B., Soleymani, S., Dawson, J., and Nasrabadi, N.M., 2021. Detection of morphed face images using discriminative wavelet sub-bands. In: 2021 IEEE International Workshop on Biometrics and Forensics (IWBF), IEEE, pp.1-6.
Asaad, A., and Jassim, S., 2017. Topological Data Analysis for Image Tampering Detection. Digital Forensics and Watermarking: 16th International Workshop, IWDW 2017, Magdeburg, Germany, August 23-25, 2017, Proceedings 16, Springer, pp.136-146.
Caldeira, E., Neto, P.C., Gonçalves, T., Damer, N., Sequeira, A.F., and Cardoso, J.S., 2023. Unveiling the two-faced truth: Disentangling morphed identities for face morphing detection. 2023 31st European Signal Processing Conference (EUSIPCO), IEEE, pp.955-959.
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., and Zisserman, A., 2018. VGGFace2: A Dataset for Recognising Faces Across Pose and Age. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), IEEE, pp.67-74.
Colbois, L., and Marcel, S., 2022. On the Detection of Morphing Attacks Generated by GANs. In: 2022 International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, pp.1-5.
Damer, N., Raja, K., Süßmilch, M., Venkatesh, S., Boutros, F., Fang, M., Kirchbuchner, F., Ramachandra, R., and Kuijper, A., 2021. ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-Generation. In: Advances in Visual Computing: 16th International Symposium, ISVC 2021, Virtual Event, October 4-6, 2021, Proceedings, Part I, Springer, pp.251-264.
DeBruine, L., and Jones, B., 2017. Face Research Lab London Set. Figshare. Dataset. Available from: https://doi.org/10.6084/m9.figshare.5047666.v4 [Last accessed on 24 April 2024].
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L., 2009. ImageNet: A large-scale hierarchical image Database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp.248-255.
Deng, J., Guo, J., Xue, N., and Zafeiriou, S., 2019. ArcFace: Additive Angular Margin Loss for Deep Face Recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.4690-4699.
Ferrara, M., Franco, A., and Maltoni, D., 2014. The Magic Passport. In: IEEE International Joint Conference on Biometrics, Clearwater, FL, USA, pp.1-7.
Hamza, M., Tehsin, S., Humayun, M., Almufareh, M.F., and Alfayad, M., 2022. A comprehensive review of face morph generation and detection of fraudulent identities. Applied Sciences, 12(24), p.12545.
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C., 2020. GhostNet: More Features From Cheap Operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp.1580-1589.
Hancock, P., 2008. Psychological Image Collection at Stirling (PICS) – 2D Face Sets – Utrecht ECVP. Available from: https://pics.stir.ac.uk [Last accessed on 24 April 2024].
He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778.
Hoang, T.T., Islam, B.M.S., and Ahn, H., 2025. Face Morphing Attack Detection Using Similarity Score Patterns Between De-Morphed and Live Images. Electronics, 14(19), p.3851. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W.,
Zhu, Y., Pang, R., Vasudevan, V., Le, Q.V., and Adam, H., 2019. Searching for MobileNetV3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp.1314-1324.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q., 2017. Densely Connected Convolutional Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4700-4708.
Huber, M., Boutros, F., Luu, A.T., Raja, K., Ramachandra, R., Damer, N., Neto, P.C., Gonçalves, T., Sequeira, A.F., and Cardoso, J.S., 2022. SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-Aware Synthetic Training Data. In: 2022 IEEE International Joint Conference on Biometrics (IJCB), IEEE, pp.1-10.
Hussein, A.R., and Rashid, R.D., 2022. KurdFace morph dataset creation using OpenCV. Science Journal of University of Zakho, 10(4), pp.258-267.
Ibrahem, A.H., and Abdelazeez, A.M., 2026. KurdFace-1000: A new multiattribute dataset for facial beauty prediction using multi-task learning. ARO-The Scientific Journal of Koya University, 14(1), pp.1-11.
Ibsen, M., Rathgeb, C., Marcel, S., and Busch, C., 2024. Multi-Channel Cross Modal Detection of Synthetic Face Images. In: 2024 12th International Workshop on Biometrics and Forensics (IWBF), IEEE, pp.1-6.
ISO/IEC, 2023. ISO/IEC 30107-3 Information Technology Biometric Presentation Attack Detection - Part 3: Testing and Reporting. International Organization for Standardization, Switzerland.
Jassim, S., and Asaad, A., 2018. Automatic Detection of Image Morphing by Topology-Based Analysis. In: 2018 26th European Signal Processing Conference (EUSIPCO), IEEE, pp.1007-1011.
Karras, T., Laine, S., and Aila, T., 2019. A Style-Based Generator Architecture for Generative adversarial Networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.4401-4410.
Kashiani, H., Sami, S.M., Soleymani, S., and Nasrabadi, N.M., 2022. Robust Ensemble Morph Detection with Domain Generalization. In: 2022 IEEE International Joint Conference on Biometrics (IJCB), IEEE, pp.1-10.
Lakshmi, A., and Wittenbrink, B., 2021. The India face set: International and cultural boundaries impact face impressions and perceptions of category membership. Frontiers in Psychology, 12, p.627678.
Ma, D.S., Correll, J., and Wittenbrink, B., 2015. The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47, pp.1122-1135.
Ma, D.S., Kantner, J., and Wittenbrink, B., 2021. Chicago face database: Multiracial expansion. Behavior Research Methods, 53, pp.1289-1300.
Ma, N., Zhang, X., Zheng, H.T., and Sun, J., 2018. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp.116-131.
Mahfoudi, G., Tajini, B., Retraint, F., Morain-Nicolier, F., Dugelay, J.L., and Marc, P., 2019. DEFACTO: Image and Face Manipulation Dataset. In: 2019 27th European Signal Processing Conference (EUSIPCO), IEEE, pp.1-5.
Makrushin, A., Neubert, T., and Dittmann, J., 2017. Automatic Generation and Detection of Visually Faultless Facial Morphs. In: International Conference on Computer Vision Theory and Applications, SciTePress, pp.39-50.
Mallick, S., 2016. Face Morph Using OpenCV — C++/Python. Available from: https://learnopencv.com/face-morph-using-opencv-cpp-python [Last accessed on 24 Apr 2024].
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W., 2005. Overview of the Face Recognition Grand Challenge. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), IEEE, pp.947-954.
Phillips, P.J., Wechsler, H., Huang, J., and Rauss, P.J., 1998. The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing, 16, pp.295-306.
Pimenta, J.A., Medvedev, I., and Gonçalves, N., 2023. Impact of Image Context for Single Deep Learning Face Morphing Attack Detection. In: 2023 International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, pp.1-6. Quek, A., 2019. FaceMorpher. Available from: https://github.com/alyssaq/ face_morpher [Last accessed 24 Apr 2024].
Raja, K., Ferrara, M., Franco, A., Spreeuwers, L., Batskos, I., De Wit, F., Gomez-Barrero, M., Scherhag, U., Fischer, D., and Venkatesh, S.K., 2020. Morphing attack detection-database, evaluation platform, and benchmarking. IEEE Transactions on Information Forensics and Security, 16, pp.4336-4351.
Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R., and Busch, C., 2019. Face recognition systems under morphing attacks: A survey. IEEE Access, 7, pp.23012-23026. Seibold, C., Samek, W., Hilsmann, A., and Eisert, P., 2017.
Detection of Face Morphing Attacks by Deep Learning. In: DIGITAL Forensics and Watermarking: 16th International Workshop, IWDW 2017, Magdeburg, Germany, August 23-25, 2017, Proceedings 16, Springer, pp.107-120.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., 2016. Rethinking the Inception Architecture for Computer Vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2818-2826.
Tan, M., and Le, Q., 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp.6105-6114.
Tapia, J., and Busch, C., 2023. AlphaNet: Single morphing attack detection using multiple contributors. In: 2023 IEEE International Workshop on Information Forensics and Security (WIFS), IEEE, pp.1-6.
Tapia, J.E., Russo, M., and Busch, C., 2025. Generating automatically print/ scan textures for morphing attack detection applications. IEEE Access, 13, pp.55277-55289. Tapia, J.E., Schulz, D., and Busch, C., 2025. Single-morphing attack detection using few-shot learning and triplet-loss. Neurocomputing, 636, pp.130033.
Thomaz, C.E., and Giraldi, G.A., 2010. A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, 28(6), pp.902-913.
Venkatesh, S., Ramachandra, R., Raja, K., and Busch, C., 2020. Single Image Face Morphing Attack Detection using Ensemble of Features. In: 2020 IEEE 23rd International Conference on Information Fusion (FUSION), IEEE, pp.1-6.
Venkatesh, S., Ramachandra, R., Raja, K., and Busch, C., 2021. Face morphing attack generation and detection: A comprehensive survey. IEEE Transactions on Technology and Society, 2(3), pp.128-145.
Zhang, H., Ramachandra, R., Raja, K., and Busch, C., 2024. Generalized singleimage-based morphing attack detection using deep representations from Vision Transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.1510-1518.
Zhang, H., Ramachandra, R., Raja, K., and Busch, C., 2026. Chatgpt encounters morphing attack detection: Zero-shot mad with multi-modal large language models and general vision models. IEEE Transactions on Biometrics, Behavior, and Identity Science, 8(2), pp.154-168.
Zhang, H., Venkatesh, S., Ramachandra, R., Raja, K., Damer, N., and Busch, C., 2021. MIPGAN—generating strong and high quality morphing attacks using identity prior driven GAN. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(3), pp.365-383.
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Copyright (c) 2026 Sheerko R. HmaSalah, Abdulbasit K. Al-Talabani, Aras T. Asaad
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Accepted 2026-09-24
Published 2026-11-04
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