MorDeephy: Face Morphing Detection via Fused Classiﬁcation

Iurii Medvedev

1 a

, Farhad Shadmand

1 b

and Nuno Gonc¸alves

1,2 c

Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal

Portuguese Mint and Ofﬁcial Printing Ofﬁce (INCM), Lisbon, Portugal

Keywords:

Face Morphing Detection, Face Recognition, Deep Learning, Convolutional Neural Networks, Classiﬁcation.

Abstract:

Face morphing attack detection (MAD) is one of the most challenging tasks in the ﬁeld of face recognition

nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing de-

tection, which implies the discrimination of morphed face images along with a sophisticated face recognition

task in a complex classiﬁcation scheme. It is directed onto learning the deep facial features, which carry in-

formation about the authenticity of these features. Our work also introduces several additional contributions:

the public and easy-to-use face morphing detection benchmark and the results of our wild datasets ﬁltering

strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated

a prominent ability for generalizing the task of morphing detection to unseen scenarios.

1 INTRODUCTION

The development of deep learning techniques in the

last decades allowed to achieve evident advances in

the area of face recognition. However, evolved and

sophisticated techniques for performing the presenta-

tion attacks continue to appear, which require the de-

velopment of new protection solutions.

Face morphing is one such image manipulating

technique. It is usually performed by blending sev-

eral (usually two) digital face images and allows to

match different persons with this synthetic image that

contains characteristics from both faces. While being

simple to implement, face morphing poses the secu-

rity risks of issuing an identiﬁcation document that

may be validated for two or more persons. Presenta-

tion attacks with face morphing usually can be hardly

detected by humans, which usually perform poorly in

matching unfamiliar faces on photos of ID and travel

documents (Medvedev et al., 2020) and by face recog-

nition software in ABC (automatic border control)

systems (Ferrara et al., 2014).

In the last years, face morphing has become a mat-

ter of research interest in academia (NIST, 2020) and

industry (Research and Service, 2020). Morphing

detection methods in facial biometric systems may

be distinguished into two pipelines depending on the

processing scenario. In no-reference morphing attack

https://orcid.org/0000-0003-2372-9681

https://orcid.org/0000-0003-4399-4845

https://orcid.org/0000-0002-1854-049X

detection algorithm receives a single image, where

morphing is detected. In practice, these methods are

directed to mitigate risks related to the false accep-

tance of manipulated images in the enrollment pro-

cess. The authentic document, which is generated

with a successfully accepted forged image, may fur-

ther help to deceive the face recognition system.

The differential morphing detection implies addi-

tional live data acquisition from an authentication sys-

tem which gives the reference information for the de-

tection algorithm. This scenario usually takes place

while passing an Automated Border Control (ABC)

system, when the recently enrolled image (which is

already accepted and printed on the ID Document) is

tested against morphing detection.

First morphing detection solutions relied on the

behaviour of local image characteristics (like tex-

ture, noise). Recent approaches usually employ deep

learning computer vision tools. However, many of

these methods utilize a straightforward learning strat-

egy that is limited by binary classiﬁcation or contrast

learning, which in our opinion is not optimal for a task

of face morphing detection and may lead to various

convergence problems.

In this work, we introduce a novel deep learn-

ing method for single image face morphing detec-

tion, which incorporates sophisticated face recogni-

tion tasks and implies utilizing a combined classiﬁ-

cation scheme (discussed in Section 3). We propose

the new strategy of face morphs classiﬁcation, which

is used for the purpose of face morphing detection.

Medvedev, I., Shadmand, F. and Gonçalves, N.

MorDeephy: Face Morphing Detection via Fused Classiﬁcation.

DOI: 10.5220/0011606100003411

In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023), pages 193-204

ISBN: 978-989-758-626-2; ISSN: 2184-4313

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

193

This novel approach leads to the usage of CNN cou-

pling, which is accompanied by a sophisticated label-

ing strategy and a speciﬁc data mining technique.

Also, we develop the public face morphing detec-

tion benchmark, which is designed to be adaptive to

the developer needs and at the same time to be simple

for comparison of algorithms of different developers.

As an additional contribution, we introduce the results

of our datasets ﬁltering strategy (image name lists),

which is described in Section 4.1.

Regarding the limitations of the work, it is im-

portant to note that at the current stage we focus

on single image morphing detection. Also, we do

not take into account redigitalized face images (by

printing/scanning), however we demonstrate that our

method allow to generalize the detection onto this

case. At the same time, we are limited to uti-

lizing landmark-based methods for performing face

morphing. GAN (Generative adversarial Network)

based methods require large computational resources

(namely for projecting images to latent space) and

at the same time, face recognition systems are less

vulnerable to presentation attacks with GAN morphs,

rather than to landmark-based morphs (Venkatesh

et al., 2020). However, we intend to cover those limi-

tations in further research.

2 RELATED WORK

To introduce our methodology, we need to discuss re-

cent advances in face morphing, face morphing detec-

tion (focusing on the no-reference scenario) and face

recognition.

2.1 Face Morphing

The generic pipeline of creating face morph from

original images includes the following steps: face fea-

tures extraction → features averaging → generating

morphed image from averaged features → optional

restoring image context (namely background).

Landmark based approaches, ﬁrst introduced by

Ferrara et al. (Ferrara et al., 2014), follow this

pipeline straightforwardly in the image spatial do-

main by the face landmark alignment, image warp-

ing and blending. Different reported morphing algo-

rithms employ variations of this strategy (Laboratory,

2018; LLC, 2010; Satya, 2016).

With recent advances in generative deep learning

approaches, several face morphing methods, which

utilize deep latent feature domain, were proposed.

The above face morphing pipeline may adapt var-

ious deep learning tools like variational autoencoders

(VAE) (Damer et al., 2018) or generative adversar-

ial networks (GANs) (Venkatesh et al., 2020; Zhang

et al., 2020).

2.2 Face Morphing Detection

Single image (no-reference) face morphing detection

algorithms usually utilise local image information and

image statistics.

Various morphing detection approaches em-

ploy Binarized Statistical Image Features (BSIF)

(Raghavendra et al., 2016), Photo Response Non-

Uniformity (PRNU), known as sensor noise (De-

biasi et al., 2018; Scherhag et al., 2019), texture

features (Ramachandra et al., 2019), local features

in frequency and spatial image domain (Neubert

et al., 2019) or complex combination of these features

(Lorenz et al., 2021; Scherhag et al., 2017).

Several deep learning approaches for no-reference

case were proposed. For face morphing detection

these approaches usually follow binary classiﬁcation

of pretrained face recognition features (Raghavendra

et al., 2017), which may be ﬁnetuned (Seibold et al.,

2017; Ferrara et al., 2021) or utilized in a combina-

tion with local texture characteristics (Wandzik et al.,

2018). Damer et al. (Damer et al., 2021) introduced a

better regularized strategy for morphing detection by

replacing the trivial binary classiﬁcation with pixel-

wise supervision. Aghdaie et al. (Aghdaie et al.,

2021) adopted the attention mechanism which is con-

trolled by wavelet decomposition.

Differential face morphing detection is a less chal-

lenging task and security risks in this scenario indeed

may be combated by increasing the discriminabil-

ity of face deep representation, which is utilized for

recognition.

Several approaches for differential detection was

recently proposed. Scherhag et al. (Scherhag

et al., 2020) followed the classiﬁcation of pretrained

deep features in differential scenario. Borghi et al.

(Borghi et al., 2021b) performed differential morph-

ing detection by ﬁnetuning the pretrained networks

in a complex setup with identity veriﬁcation and ar-

tifacts detection blocks. Rather different approach to

the differential scenario was introduced by Ferrara et

al. (Ferrara et al., 2018) who proposed an approach

to revert morphing by retouching the testing face im-

age with a trusted live capture to reveal the identity of

the legitimate document owner.

In comparison to the considered approaches, we

propose to focus our method on learning the authen-

ticity of deep face features, regularizing the morphing

detection with a delicate face recognition task.

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

194

Categorical

Batch 1

Morphing

y = n000002

y = n005206

(bona

de)

Feature

Layer f

Feature

Layer f

Dot Product

& sigmoid

activation

Cross entropy

Batch 2

(face morph)

y = n000002

y = n005206

Class

Layer 1

Class

Layer 2

Categorical

Cross entropy

y = n000002

Binary

Cross entropy

...

De ne

cross label t

Weighted

Sum

First Network

Second Network

Figure 1: Schematic of the proposed method. For simplicity of visualization batch contains a single image. Labels ˙y and ¨y are

indicated by names, when the real setup utilizes their numerical index value, which is encoded to one-hot vector.

2.3 Face Recognition

Modern face recognition approaches rely on deep

learning tools, which give the ability to learn

highly discriminative features themselves from un-

constrained images. Among several techniques to

perform the tasks of extracting features, the convo-

lutional neural network (CNN) is one of the most

efﬁcient for the pattern recognition problems (Rus-

sakovsky et al., 2015).

There are several strategies for approaching face

recognition via deep learning. However, all of them

are focused on extracting low-dimensional face repre-

sentation (deep face features) and increasing the dis-

criminative power of that representation.

Metric learning methods are directed on optimiz-

ing the face representation itself through the con-

trast of match/non-match pairs (Chopra et al., 2005;

Schroff et al., 2015). However, for reliable con-

vergence, these methods require enormously large

datasets and sophisticated sample mining techniques.

Another concept (which we indeed follow in our

work) is learning face representation implicitly via

a closed-set identity classiﬁcation task. Deep net-

works in these methods encapsulate face representa-

tion in the last hidden layer and usually adopt softmax

loss and its modiﬁcations for classiﬁcation (Sun et al.,

2014; Sun et al., 2014; Sun et al., 2015).

Improvement of the performance in this approach

was achieved by various techniques for increasing

intra-class compactness and maximizing inter-class

discrepancy. For example, by applying additional

regularization for pushing intra-class features to their

centre (Wen et al., 2016), or by introducing several

kinds of marginal restrictions for inter-class variance

(Liu et al., 2017; Wang et al., 2018; Deng et al., 2019;

Sun et al., 2020).

Several recent works were directed onto investi-

gating sample speciﬁc learning strategies, which are

controlled by sample quality (Tremoc¸o et al., 2021),

hardness (Zeng et al., 2020; Huang et al., 2020), data

augmentation (Shi et al., 2020) or even by treating fa-

cial representation in distributional manner (by speci-

fying sample uncertainty) (Shi and Jain, 2019).

In our work, we consider face morphing detec-

tion from the perspective of face recognition. In the

case of following the approach via identity classiﬁ-

cation, face morphing introduces a problem, since a

face morph image indeed belongs to several identities,

which leads to the ambiguity of proper class labeling.

In this work, we address this issue (Section 4.2) in

search of the method for single image morphing de-

tection.

3 METHODOLOGY

In this section, we describe our technique for single

image morphing detection via deep learning.

In our research, we intended to design a setup,

which will allow to learn high-level deep features, that

also carry information about their authenticity. We

deﬁned two requirements to our setup. First, each in-

MorDeephy: Face Morphing Detection via Fused Classiﬁcation

195

put image must be classiﬁed explicitly and unambigu-

ously. Second, the face morphing detection decision

is made by the behavior of deep face features.

This resulted in the schematic that includes two

backbone CNN based networks that are trained in a

similar manner but biased in a way to discriminate

morphed and bona ﬁde images. Namely, our idea im-

plies training two parallel networks, which consider

bona ﬁde samples similarly and morphed samples dif-

ferently (see Fig. 1). We point out that these networks

do not share the same weights and we do not intend

to train them in a contrastive manner (by matching

positive and negative pairs).

In our setup both networks learn high-level fea-

tures via classiﬁcation tasks, which are different in

terms of identity labeling of face morphs. First Net-

work labels them by the original identity from the ﬁrst

source image, the Second Network - by the second

original label.

The extracted features are also explicitly com-

pared by similarity metric (which is the dot product

due to the softmax properties) and the result is clas-

siﬁed according to the ground truth authenticity label

of the image (bona ﬁde/morph). Indeed the variance

of the behavior of the networks is used to make the

detection decision.

The identity classiﬁcation parts of the training

scheme act as a regularization that retains the facial

discriminability of feature layers. That is why for

identity classiﬁcation we utilize a standard softmax,

which allows easier convergence in comparison with

its modiﬁcations (like ArcFace(Deng et al., 2019)).

Following the common formulation of softmax,

our training process is regularized by the losses:

= −

∑

log(

˙y

∑

˙y

) (1)

= −

∑

log(

¨y

∑

¨y

), (2)

where





denote the deep features of the i − th

sample,

{

˙y

, ¨y

}

are the numerical class indexes of the

i −th sample,



W ,



and





are weights and bi-

ases of last fully connected layer (respectively for the

{

First,Second

}

networks). N is the number of sam-

ples in a batch and C is the total number of classes.

The target driver of the training process explic-

itly discriminates morph/non-morph images. The dot

product of backbones outputs indicates the morphing

detection score. It is activated by sigmoid function,

and the corresponding loss is deﬁned as binary cross-

entropy:

= −

∑

t log

1+e

−D

+ (1 −t)log



1 −

1+e

−D



(3)

where closs-label t is computed by a comparison of

input class labels:

t = abs(sgn( ˙y

− ¨y

)), (4)

and D is a dot product of high level features extracted

by First and Second backbones:

D =

f ·

f (5)

The result loss for optimization is combined as a

weighted sum:

L = α

+ α

+ βL

(6)

By minimizing this loss in the fused classiﬁcation

setup, we learn the discriminative facial features that

are explicitly regularized for morphing detection. The

optimal values of weighting coefﬁcients α

, α

and β

will be obtained empirically in the Section 6.1.

At the testing stage, the identity classiﬁcation

parts of the network are redundant and may be re-

moved from the setup. The morphing detection de-

cision is made by thresholding the scalar product of

the backbones outputs.

4 DATASETS

The proposed methodology requires the large labeled

face dataset with an accompaniment of morphed im-

ages of identities from this dataset.

The academic community still doesn’t have pub-

lic ID document compliant datasets which are large

enough for efﬁcient training of modern deep networks

(as an example, one of the largest FRGC V2(Phillips

et al., 2005) contains only ∼50k images and ∼500

identities). That is why our strategy for this work is

to utilize the wild dataset which is ﬁltered by the cri-

teria of suitability for face morphing. Conceptually

this approach indeed is not novel and was recently uti-

lized in face morphing research (Damer et al., 2019;

Damer et al., 2021). In this work, we introduce a tech-

nique for semi-automatic wild dataset ﬁltering for our

method.

As a source wild dataset we use VGGFace2(Cao

et al., 2018)(∼3M images, ∼9k classes, ∼360 sam-

ples per class, License - CC BY-SA 4.0) due to large

average number of samples per identity (in compari-

son to other popular wild face datasets like CASIA-

WebFace(Yi et al., 2014), MS-Celeb-1M(Guo et al.,

2016; Jin et al., 2018), Glint360K(An et al., 2021),

WebFace260M(Zhu et al., 2021)) in order to have

enough samples per identity after ﬁltering.

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196

Figure 2: FAR and FRR for result manual quality labeling

of random samples from VGGFace2.

4.1 Wild Dataset Filtering

Our dataset ﬁltering strategy is based on a thresh-

olding by quality metrics. Following Tremoc¸o et

al. (Tremoc¸o et al., 2021) we used a set of qual-

ity scores for labeling the face images in the dataset:

Blur (Bansal et al., 2016), FaceQNet (Hernandez-

Ortega et al., 2019), BRISQUE (Mittal et al., 2012),

Face Illumination (Zhang et al., 2017) and Pose

(Ruiz et al., 2018). This set of scores allow to dis-

criminate and select samples by their natural quality

(Blur, BRISQUE), ID documents suitability (Face-

QNet), face image acquisition parameters (Illumina-

tion, Pose).

Next, we randomly select samples and manually

label them with a binary value (accept/reject). This

acceptance is deﬁned by the criteria of suitability

for application in face morphing (namely by user’s

choice). In our setup, we assure that samples are se-

lected distributively across the quality scores values.

Namely, we split the total quality score range into a

set of sub-ranges and deﬁne the minimum number of

samples to be selected from each sub-range. By pro-

ceeding around 4k images in our setup, we harvest

the dependency of FAR (False Acceptance Rate) and

FRR (False Rejection Rate) from quality scores val-

ues (see Fig. 2).

The dataset ﬁltering is then performed with joint

thresholding by those quality metrics. For each score,

we select the threshold at a point of EER (Equal Error

Figure 3: Example of VGGFace2 ﬁltering result. Accepted

images (green box) and Rejected images (red box).

Rate). To combine a ﬁltered dataset we collect sam-

ples, which have a quality score value higher then the

corresponding threshold for each quality metric. As a

result we get the VGGFace2-selected dataset with the

same identity list as original source and around 500k

images (see Fig. 3).

4.2 Morph Dataset Harvesting Strategy

For application in our method, the ﬁltered wild dataset

is needed to be accompanied by a large collection of

face morphs. We automatically generate these im-

ages with our customized landmark-based morphing

approach (with blending coefﬁcient 0.5) (see Fig. 4).

A key requirement for effective learning is to pro-

vide unambiguity of proper class labeling in our train-

ing method. Namely after generating face morph

from two arbitrary samples of the original dataset, the

resulting image indeed belongs to both source iden-

tities. That is why fully random image pairing (for

generating morphs) will result in classiﬁcation confu-

sion.

To avoid that we utilize the following strategy.

First, we separate the total list of identities into two

disjoint parts, which are attributed to the First and

the Second networks respectively. Next, to generate a

face morph, we randomly pair images from identities

of these list halves. Each generated image is then la-

beled according to the attributed sublist for classiﬁca-

tion by the First and the Second networks. Let us note

that this labeling is made differently for each morphed

image and similarly for bona ﬁde images in both net-

works (see Fig. 1). That is why this technique, which

primarily acts as a regularization, also ampliﬁes the

morphing detection performance.

The separation of a dataset to two disjoint iden-

tity sets is performed to assure that morphed combina-

tions of a particular identity are classiﬁed similarly by

the networks. For example, if the dataset include four

identities [A,B,C,D], we split it to [A,B] (assigned for

the First network) and [C,D] (assigned for the Sec-

ond network) and generate possible paired morphs:

MorDeephy: Face Morphing Detection via Fused Classiﬁcation

197

[AC], [BC], [AD], [BD], which may be unambiguously

classiﬁed by both networks by their assigned identity

lists. For instance, in this case, the combination [AB]

is hardly classiﬁed. Indeed we assign half of the iden-

tities as main to both networks. Without such separa-

tion, the overall classiﬁcation loses its regularisation

sense.

Following the above procedure, we generate

VGGFace2-selected-morph dataset, which contains

around 1M morphed images.

→

Figure 4: Examples of generated morphs with landmark

based approach. Background is restored by one of the

source images (chosen randomly).

Figure 5: Examples of generated selfmorphs. Images con-

tain blending artefacts but the identity is perceptually re-

tained.

4.3 Selfmorphing

The fully automatic landmark morphing methods may

introduce a number of visible artefacts to the gener-

ated images (like blending artefacts). That is why

without additional regularisation our method will be

biased to learning those artefacts, which is not a real-

istic scenario. Real fraudulent morphs are retouched

with the intention to remove any perceptual artefacts.

To address this problem, we utilize selfmorphs,

which are generated by applying face morphing to im-

ages of the same identity. This concept was indeed

recently introduced by Borghi et al. (Borghi et al.,

2021a) and was used for generating images with vis-

ible artefacts. Then for removing these artefacts the

authors trained the Conditional GAN using original

images as a ground truth reference.

In this work, we utilize selfmorph images to fo-

cus morphing detection onto the deep face features

behaviour, rather than to detecting artefacts. We as-

sume that deep discriminative face features remain af-

ter performing selfmorphing. In the proposed method

schematic (Fig. 1) we consider selfmorphs as bona

ﬁde samples.

We perform a random pairing of samples within

each identity from the VGGFace2-selected and gen-

erate VGGFace2-selected-selfmorph dataset, which

contains around 500k images (see Fig. 5).

5 BENCHMARKING

There are few public benchmarks for evaluating the

performance of morphing detection or morphing re-

sistant algorithms: the NIST FRVT MORPH (NIST,

2022) and FVC-onGoing MAD(Raja et al., 2020;

Dorizzi et al., 2009). Both of these benchmarks

accept no-reference and differential morphing algo-

rithms, however, they are proprietary and are executed

on the maintainer side. Thus they have a number of

submission restrictions.

The straightforward metric for evaluating single

image morphing detection is the dependency of Bona

ﬁde Presentation Classiﬁcation Error Rate (BPCER)

from Attack Presentation Classiﬁcation Error Rate

(APCER) (according to ISO/IEC 30107-3 (Inter-

national Organization for Standardization, 2017)),

which may be plotted as a Detection Error Trade-off

(DET) curve.

5.1 Face Morphing Detection

Benchmark

For this work, we intend to develop and provide an

easy-to-use benchmark, which is to be executed on

the developer side (It will be made public in case of

paper acceptance). The existing public benchmarks

provide useful data but usually specify the protocols

for the certain software frameworks (Sarkar et al.,

2020).

Our benchmark intends to provide the function-

ality for estimating the morphing detection perfor-

mance, for generating custom protocols and also for

further comparison of the results from different de-

velopers with existing protocols. At this stage of our

work, we focus on the single image morphing de-

tection with only the usage of public data (however,

we assume the possibility of further adapting private

datasets).

We generate several protocols for single image

morphing detection. Our benchmark is based on

the FRGC-Morphs, FRLL-Morphs (Sarkar et al.,

2020), AMSL (Neubert et al., 2018) and Dustone

datasets(Dustone, 2017). Using this data we com-

bine several benchmark protocols with various types

of face morphs:

• protocol-real (∼3k morphs(Dustone+AMSL)),

which includes morphs with low level of visible

blending artifacts, and imitates realistic presenta-

tion attacks.

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

198

Figure 6: Detection Error Trade-off curves for various α/β proportions in different protocols.

Figure 7: Detection Error Trade-off curves for various bona ﬁde images selection in different protocols.

Table 1: APCER@BPCER = (0.1, 0.01) of our method for various α/β proportions and bona ﬁde images selection in different

protocols. Org. and SM. correspond to cases where only original and selfmorph images respectively were chosen as bonaﬁde

samples.

Method

APCER@BPCER = δ

real facemorpher webmorph stylegan

δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01

α = 0 0.697 0.947 0.756 0.939 0.976 0.995 0.980 0.998

α/β = 0.01 0.601 0.895 0.651 0.957 0.945 0.992 0.895 0.991

α/β = 0.05 0.607 0.965 0.502 0.968 0.915 0.999 0.842 0.996

α/β = 0.2 0.401 0.835 0.431 0.786 0.778 0.979 0.799 0.969

α/β = 1.0 0.711 0.935 0.670 0.928 0.942 0.995 0.913 0.982

α/β = 10.0 0.556 0.839 0.513 0.791 0.749 0.923 0.852 0.969

β = 0.0 0.945 0.996 0.922 0.993 0.954 0.997 0.949 0.997

α/β = 0.2 Org. 0.481 0.875 0.498 0.876 0.987 0.997 0.915 0.993

α/β = 0.2 SM. 0.608 0.938 0.568 0.911 0.704 0.986 0.816 0.983

Table 2: BPCER@APCER = (0.1, 0.01) of our method for various α/β proportions and bona ﬁde images selection in different

protocols. Org. and SM. correspond to cases where only original and selfmorph images respectively were chosen as bonaﬁde

samples.

Method

BPCER@APCER = δ

real facemorpher webmorph stylegan

δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01 δ = 0.1 δ = 0.01

α = 0 0.795 0.993 0.781 0.971 0.961 0.998 0.952 0.998

α/β = 0.01 0.625 0.968 0.638 0.979 0.969 0.997 0.991 1.0

α/β = 0.05 0.577 0.950 0.598 0.951 0.841 0.989 0.895 0.991

α/β = 0.2 0.494 0.726 0.562 0.848 0.710 0.822 0.697 0.904

α/β = 1.0 0.616 0.871 0.628 0.843 0.882 0.963 0.851 0.960

α/β = 10.0 0.642 0.892 0.604 0.913 0.742 0.931 0.829 0.967

β = 0.0 0.956 0.998 0.932 0.998 0.971 0.998 0.874 0.986

α/β = 0.2 Org. 0.417 0.601 0.370 0.595 0.718 0.963 0.605 0.862

α/β = 0.2 SM. 0.580 0.912 0.587 0.905 0.484 0.842 0.798 0.976

MorDeephy: Face Morphing Detection via Fused Classiﬁcation

199

• protocol-facemorpher (∼2k morphs), which in-

cludes simple morphs with foreground and back-

ground artifacts

• protocol-webmorph (∼1k morphs), which in-

cludes images with background artifacts but the

low level of artifacts inside the face contour

• protocol-stylegan (∼2k morphs), which includes

StyleGan morphs

As bona ﬁde images all our protocols use frontal

faces from the following public datasets: FRLL

Set(DeBruine and Jones, 2017), FEI (Artiﬁcial In-

telligence Laboratory of FEI in S

ao Bernardo do

Campo, S

ao Paulo, Brazil, 2006), AR(Martinez and

Benavente, 1998), Aberdeen and Utrecht (School of

Natural Sciences University of Stirling, 1998) (∼1.5k

images in total).

Since our benchmark utilities are not public yet

other developers cannot contribute their results to per-

form SOTA comparison for this work. That is why

we use our protocols only for performing the ablation

study and ﬁnding the best settings for our approach.

6 EXPERIMENTS AND RESULTS

To analyze the performance of our approach we

make several experiments with our method. As

backbone networks we use ResNet-50 (He et al.,

2016), which are initialized with weights, pretrained

on the ImageNet dataset. Followed by pooling

and dropout layers each backbone returns 512 deep

features. To complete the networks for classiﬁca-

tion we add the classiﬁcation layer with the size

= C equal to the number of classes in the train-

ing dataset. Input images (RGB 3-channel) are

aligned and resized to 224×224. In all our exper-

iments we perform training with SGD (Stochastic

Gradient Descent) optimizer for 2 epochs, decreas-

ing learning rate from 0.001 to 0.0001. We report the

performance by APCER@BPCER = (0.1,0.01) and

BPCER@APCER = (0.1,0.01).

Our default training dataset is a joined and shuf-

ﬂed concatenation of the above mentioned datasets:

VGGFace2-selected, VGGFace2-selected-selfmorph,

and VGGFace2-selected-morph.

It is important to note that in all experiments we

assured the equal balance between the numbers of

morphed and non-morphed (which are bona ﬁde +

selfmorphed) images in the training dataset.

6.1 Fused Classiﬁcation Balance

For effective convergence and further morphing de-

tection, our method requires choosing the proper

balance between the elements of the loss function.

Namely the balance between α (= α

= α

) and β

(disbalance of α

and α

didn’t demonstrate any in-

teresting behaviour in our tests). We perform train-

ing of our method with different proportional settings

also including the ablation of particular parts from the

overall loss. Our experiments demonstrate (see Fig.

6 and Tab. 1, 2) that by varying α/β proportion it

is possible to achieve some optimal performance of

morphing detection in different protocols. Our strat-

egy allows generalizing the detection of morphing

even to the images, which are generated with GANs

even accounting that this type of morphing is totally

unseen in the training.

On the edge case with excluded main loss func-

tion driver (namely binary morph/bona ﬁde classiﬁ-

cation), our method demonstrates the almost random

detection decision. At the same time, ablation of the

regularization (β = 0) also leads to bad performance,

which we relate with the overﬁtting on the trivial bi-

nary classiﬁcation learning task.

Summing up, we can conclude that our strategy

allows learning such face features which are discrim-

inative by the criteria of authenticity.

6.2 Data Combination Experiments

Further experiments are performed with the selected

proportion α/β = 0.2 in order to understand the im-

pact of selfmorphing for our method.

In comparison to the dataset selection in Section

6.1, where the collection of bona ﬁde samples is split

evenly to original and selfmorphs, we test two more

options where these particular parts are ablated from

the total dataset.

Our results (see Fig. 7 and Tab. 1,2) proves the

signiﬁcant importance of selfmorphs in our strategy.

Utilizing selfmorphs at the training stage allows to re-

duce the emphasis of the detection of facial blending

artefacts and shift it to the behavior of the deep feature

for generalizing to unseen types of attacks.

6.3 NIST FRVT Morph Results

We have performed the comparison of the results of

our method and the state of the art face morphing de-

tection approaches with NIST FRVT MORPH Bench-

mark (NIST, 2022).

We select the model with α/β = 0.2 from Section

6.1 as our best model and present results of compari-

son in several protocols:

• P1 - Visa-Border (25727 Morphs)

• P2 - Manual (323 Morphs)

• P3 - MIPGAN-II (2464 Morphs)

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

200

Table 3: Comparison with the state of the art single image morphing detection methods by APCER@BPCER = (0.1, 0.01).

Method

APCER@BPCER = δ

P1 P2 P3 P4 P5

δ = δ = δ = δ = δ = δ = δ = δ = δ = δ =

0.1 0.01 0.1 0.01 0.1 0.01 0.1 0.01 0.1 0.01

(Aghdaie et al., 2021) 0.172 0.782 0.965 0.998 0.923 0.991 0.015 0.200 0.271 0.721

(Debiasi et al., 2018) 0.994 0.996 0.049 0.823 0.994 1.000 1.000 1.000 0.985 0.994

(Ramachandra et al., 2019) 0.659 0.996 0.375 0.990 0.938 0.985 0.159 0.998 0.936 0.996

(Scherhag et al., 2018) 0.986 1.000 1.000 1.000 0.997 1.000 0.996 1.000 0.993 1.000

(Lorenz et al., 2021) 0.844 1.000 0.380 1.000 0.966 1.000 0.819 1.000 0.971 0.995

(Ferrara et al., 2021) 0.982 0.996 0.477 0.999 0.978 1.000 0.037 0.810 0.420 0.777

Ours 0.419 0.658 0.434 0.686 0.842 0.954 0.323 0.639 0.499 0.805

• P4 - Print + Scanned (3604 Morphs)

As bona ﬁde samples all protocols utilize

a large collection of 1047389 Bona Fide im-

ages. The comparison is performed by the metrics

APCER@BPCER = (0.1, 0.01).

6.4 Single Image MAD

We perform the comparison in the target single image

morphing detection scenario (see Table 3).

MorDeephy outperforms other techniques in de-

tecting landmark-based morphs and challenging man-

ual morphs and achieves comparable results in other

protocols.

Also, our method does not demonstrate bias to a

particular morphing generative strategy and has the

most stable performance across all protocols in com-

parison to other approaches.

It is important to note that these results are

achieved by utilizing a rather straightforward and sim-

ple morphing technique during training (without any

adaptation to realistic scenario or modiﬁcations for re-

moving artefacts), which proves that our method al-

lows generalizing morphing detection to various un-

seen generative approaches by focusing on deep face

features behavior.

7 CONCLUSION

We introduce a novel deep learning strategy for single

image face morphing detection, which implies utiliz-

ing a complex classiﬁcation task. It is directed onto

learning the deep facial features, which carry infor-

mation about the authenticity of these features. Our

method achieved the state of the art performance and

demonstrated a prominent ability for generalizing the

task of morphing detection to unseen scenarios (like

GAN morphs and print/scan morphs).

Our work also introduces several additional con-

tributions, which are the public and easy-to-use face

morphing detection benchmark and the results of our

wild datasets ﬁltering strategy.

In our further work, we will focus on improv-

ing the performance by applying more sophisticated

morphing techniques during training and on explicit

adapting our method to the differential scenario,

which will require sophisticated sampling strategies.

ACKNOWLEDGEMENTS

The authors would like to thank the Portuguese Mint

and Ofﬁcial Printing Ofﬁce (INCM) and the Institute

of Systems and Robotics - the University of Coimbra

for the support of the project Facing. This work has

been supported by Fundac¸

ao para a Ci

encia e a Tec-

nologia (FCT) under the project UIDB/00048/2020.

The computational part of this work was performed

with the support of NVIDIA Applied Research Accel-

erator Program with hardware and software provided

by NVIDIA.

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