In this paper we examine how pre-processing and training methods impact on the performance of Lightweight CNNs through evaluations on MobileNetV3 with a spoofing detection head dubbed "MobileNetV3-Spoof". Using the UniAttackData dataset from the 5th Face Anti-Spoofing Challenge@CVPR2024 which covers a broad spectrum of spoofing scenarios including deepfake and adversarial attack samples we assess how well the model performs over different setups including pre-trained models and models trained from scratch with or without initial face detection and alignment. Our results show that pre-processing steps significantly boost the model's ability to identify spoof samples especially against complex attacks. Through detailed comparisons we offer insights that could guide data curation and the creation of more effective and efficient anti-spoofing techniques suitable for real-world use in the era of digital face attacks. We make our code publicly available at: https://github.com/Inria-CENATAV-Tec/Assessing-Efficient-FAS-CVPR2024