This is the repository that contains UBO-SMAD-R3, a standalone version of an Inception-ResNet V1, fine-tuned for Single-image Morphing Attack Detection (S-MAD). This model was trained using the Revelio framework.
The required packages are present in the requirements.txt file. To install them, run the following command:
pip install -r requirements.txtThe inception_resnet_smad package exposes a get_prediction function which, in its simplest form, takes in input a document image, and returns a morphing prediction.
0 means that the document image is bona fide, while 1 means that the document image is morphed.
from ubo_smad_r3 import get_prediction
import cv2 as cv
# Load the document image
document = cv.imread("document.png")
# Get the prediction
prediction = get_prediction(document)This function also allows the user to specify the device to use for the computation (i.e. CPU or GPU) with the optional device parameter. The default value is cpu.
from ubo_smad_r3 import get_prediction
import cv2 as cv
# Load the document image
document = cv.imread("document.png")
# Get the prediction
prediction = get_prediction(document, device="cuda:0")Finally, the function supports computing batched predictions, by passing a list containing the document images. The function will return a list of predictions.
from ubo_smad_r3 import get_prediction
import cv2 as cv
# Load the document images
documents = [cv.imread("document1.png"), cv.imread("document2.png")]
# Get the predictions
predictions = get_prediction(documents, device="cuda:0")When using the code from this repository, please cite the following work:
@article{borghi2023revelio,
title={Revelio: a Modular and Effective Framework for Reproducible Training and Evaluation of Morphing Attack Detectors},
author={Borghi, Guido and Di Domenico, Nicol{\`o} and Franco, Annalisa and Ferrara, Matteo and Maltoni, Davide},
journal={IEEE Access},
year={2023},
publisher={IEEE}
}