# A Study of the Human Perception of Synthetic Faces

Bingyu Shen1, Brandon RichardWebster2, Alice O’Toole3, Kevin Bowyer1
and Walter J. Scheirer1
1 Department of Computer Science and Engineering, University of Notre Dame,
Notre Dame, IN, USA 2 Kitware, Inc., Clifton Park, NY, USA 3 School of
Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson,
TX, USA

###### Abstract

Advances in face synthesis have raised alarms about the deceptive use of
synthetic faces. Can synthetic identities be effectively used to fool human
observers? In this paper, we introduce a study of the human perception of
synthetic faces generated using different strategies including a state-of-the-
art deep learning-based GAN model. This is the first rigorous study of the
effectiveness of synthetic face generation techniques grounded in experimental
techniques from psychology. We answer important questions such as how often do
GAN-based and more traditional image processing-based techniques confuse human
observers, and are there subtle cues within a synthetic face image that cause
humans to perceive it as a fake without having to search for obvious clues? To
answer these questions, we conducted a series of large-scale crowdsourced
behavioral experiments with different sources of face imagery. Results show
that humans are unable to distinguish synthetic faces from real faces under
several different circumstances. This finding has serious implications for
many different applications where face images are presented to human users.

††publicationid: pubid: 978-1-6654-3176-7/21/$31.00 ©2021 IEEE

##  I Introduction

Face synthesis is a popular computer vision approach with many creative
applications, but a few potentially dangerous ones as well. The purpose of
face synthesis is to generate photo-realistic face images of non-existent
identities from real face input, and several generative methods have achieved
great success in this endeavour [12, 16, 22]. This family of techniques
includes, but is not limited to, face swapping [5, 26, 25], face aging [10,
23], and face expression synthesis [41]. Recently, the resolution and quality
of synthetic human faces has drastically improved thanks to the development of
generative adversarial networks (GANs) [16, 34, 42]. Remarkably, the
plausibility of generated faces from these techniques has not been assessed in
any rigorous way. Is it possible for these approaches to fool human observers?
Besides the quality of generated face images, other factors that might affect
a human observer’s decision when distinguishing between synthetic and real
faces remain unclear.

Figure 1: Can you tell which face in the above set is from a real identity?
The pace of face synthesis research has accelerated rapidly, and it is now
possible for anyone to create photo-realistic faces of non-existent identities
using accessible software tools. This has been good for creatives on the
Internet, but potentially problematic for ordinary people who may encounter a
fake face as part of a scam or some other deceptive practice. In this paper,
we study the human ability to distinguish between real and synthetic faces.

Motivated by these issues, in this work we studied the human perception of
synthetic faces generated by different algorithms using methods from
experimental psychology. The primary question we’re interested in is: how well
can humans distinguish between synthetic faces and real faces? This is of
great importance because of the increasing use of synthetic faces in espionage
[33], propaganda [30], and online trolling [27]. For example, such technology
might bring severe consequences if deployed in a deceptive manner in video
conferencing, as this communication tool is playing a key role in our lives
during the COVID-19 pandemic. Websites such as thispersondoesnotexist.com have
gained increasing attention, showing the power of GANs in creating synthetic
identities with fine details. Datasets of synthetic face images and videos
have also been released to accelerate the development of deep learning
algorithms to detect this type of content [6, 19, 31, 32].

There are several aspects of the visual appearance of the face that we are
interested in assessing in this work. We know that synthetic faces look
realistic at first glance, but can people distinguish them from real human
faces when both are presented together? In Fig. 1, only the face that is
second from the left is from a real identity. The other face images are all
generated by face synthesis techniques. The two images on the right are
generated by StyleGAN [16], while the leftmost image is generated by SREFI
[4]. Identifying the real face from these images is an extremely difficult
task. There are multiple reasons for that.

First, the quality of synthetic faces has achieved a level whereby generated
faces possess a significant number of high-quality details such as skin
texture, hair, and wrinkles. Irregularities in such details once served as
clues that people would try to find when attempting to distinguish artificial
faces from real ones. Second, in the study introduced in this paper, human
raters are presented with images of varying gender, race, and age, as in Fig.
1, which makes the task even harder. However, this scenario mimics the
deployment of synthetic faces in the real-world, where they can be mixed with
real faces on social media and elsewhere. Third, the context in which a face
appears is another confound. To what extent do overall scene conditions such
as the background or lighting affect human judgement of real and synthetic
faces?

To understand the human perception of synthetic faces, we designed four
behavioral experiments with different elements that could be controlled for
(e.g., background, lighting, subject identities). Alarmingly, statistical
analysis of the collected data shows that the human capacity to distinguish
between real and synthetic faces is no better than chance at best. We also
look at potential information leakage in synthetic faces. Since synthetic
faces are always generated using face images of real identities as part of the
process, is it possible that a resulting synthetic face can reveal the real
identity that contributed to it in some capacity? And will human observers
notice?

This work offers a thorough and in-depth evaluation of the human perception of
synthetic faces that is different from existing studies in several regards:

  1. 1.

Different face synthesis techniques are utilized including a state-of-the-art
GAN-based method, which makes our analysis more comprehensive in scope.

  2. 2.

Instead of relying on a single source for synthetic and real face images, a
new large-scale dataset is created from multiple sources of face images,
giving our study greater image diversity.

  3. 3.

The study of human perception of synthetic faces is grounded in established
methods and procedures from experimental psychology, giving the analysis a
level of rigor missing from prior studies.

  4. 4.

Large-scale crowdsourced behavioral experiments were conducted using hundreds
of human subjects, making this one of the largest studies of its kind.

In the rest of this paper, we review the literature (Sec. II) on traditional
image processing-based face synthesis, current machine learning-based face
synthesis, and existing studies of the human perception of synthetic faces. We
then go on to discuss the sources of data used in our study (Sec. III), as
well as the experimental protocols and results (Sec. IV). We conclude with
some thoughts on the landscape of synthetic media, and ideas for mitigating
the impact of synthetic faces when they are used in questionable
circumstances.

##  II Related Work

Face synthesis has been an active computer vision research area for decades.
The main goal of face synthesis is to generate realistic new identities from
images of real faces.

###  II-A Traditional face generation methods

There are multiple ways to combine two or more real faces into a new face [5,
24, 8]. Bitouk [5] et al. proposed a system for face swapping and created a
large library of face images that served as a pool of candidates for swapping
operations. To swap a target face, the system picks candidates that most
resemble the target face and adjusts them in pose, lighting, and color to
match the input. After blending the adjusted faces into the target face, the
system will rank the output images generated by each candidate via a boundary
metric and pick the best one. The boundary metric measures the perceptual
similarity to the input image along the boundary of the replacement region. A
user study suggested that the generated synthetic faces are frequently
mistakenly identified as real.

Mosaddegh et al. [24] defined different regions, such as the eyes, eyebrows,
and nose of faces as face components. To synthesize a new face from an input
face, they borrow different face components from up to four donor faces and
replace the respective regions in the original input face image with these
borrowed components.

Banerjee et al. [4] proposed a similar approach dubbed “synthesis of realistic
example face images” (SREFI), which constructs synthetic face images from a
set of face images from real identities. SREFI triangularizes faces, while
keeping important facial features away from triangle corners, and then
stitches and blends together triangles combined from a set of different face
images.

Other proposed methods have involved embedding faces into high-dimensional
spaces, allowing faces to be synthesized by traversing the resulting manifold
[13].

###  II-B Machine learning-based face generation

Compared to the traditional methods, machine learning-based techniques that
make use of deep neural networks show significant advantages in image quality
and flexibility of use. Among the various deep neural networks in the
literature, GANs have shown their superiority in generating realistic objects
from different inputs [14].

Leveraging this early success, GANs have been successfully applied to the
specific object category of human faces. Song et al. [34] proposed a geometry-
guided GAN for photo-realistic and identity-preserving facial expression
synthesis. Wang et al. [37] proposed a novel U-Net Conditional GAN and an
identity-preserving loss for facial expression generation. Antipov et al. [1]
proposed a GAN-based method for automatic identity-preserving face aging.
Zhang et al. [40] were able to use a GAN-based multi-stream feature-level
fusion technique to generate high-quality face images from the input of
polarimetric thermal images. To solve the problem of identifying faces with
significant out-of-plane rotation, Han et al. [11] proposed a GAN with
tripartite adversaries to synthesize the frontal view from various poses.

Beyond generating new instances of the same identity, GANs have been widely
used to generate ‘random’ faces for new synthetic identities. Yuan et al. [39]
proposed an attribute aware face generator with a GAN that can generate face
images according to specific characteristics corresponding to given
attributes. StyleGAN [16] can disentangle the latent space of the styles of
face images (e.g., hair styles, face shapes, eye colors), therefore providing
a more controlled process of face synthesis.

###  II-C Studies on the human perception of synthetic faces

Studies of the human perception of synthetic faces have been conducted ever
since computer graphics matured to the point of being able to generate photo-
realistic objects. For example, Farid and Bravo [9] conducted psychological
experiments that explored the impact of face image resolution, compression,
and color on the human ability to distinguish synthetic faces from real face
images. Balas and Pacella [3] studied the difference in the perception of
trustworthiness in real faces and synthetic faces. Their results showed that
the absolute levels of perceived trustworthiness and relative trustworthiness
judgements are both affected by synthetic faces. The same group also conducted
experiments related to memory during human interactions with artificial faces
[2]. Results indicated it is easier to remember real faces.

After the emergence of high-quality synthetic face generation powered by deep
neural networks, more studies appeared that focused on both human perception
and the algorithms related to the face generation methods. Korshunov and
Marcel [20] conducted a study of the human perception of deepfake videos.
Human raters were asked a question about whether the face in a video is real
or synthetic. Specifically, human perception was compared with the state-of-
the-art deepfake detection algorithms. In this context, deepfakes are edited
videos where one person’s face is inserted onto the body of another person.
The deepfake videos are split into different scales in terms of difficulty.
The results show that there is a difference between human detection ability
and deepfake detection algorithms, and that deepfake videos are able to
confuse the majority of the public at the present moment. Lago et al. [21]
conducted a similar study on synthetic face images via crowdsourced
experiments.

##  III Data sources for Real and Synthetic Faces

In this work we analyzed human behavior in distinguishing between synthetic
faces and real faces. To the best of our knowledge, this is the first study
that quantifies the quality of synthetically-generated faces in real-world
settings with respect to plausibility.

There are three types of face images used in our experiments: real faces,
synthetic faces generated by SREFI [4], and synthetic faces generated by
StyleGAN2 [17].

####  III-1 Real faces

Figure 2: Examples of real faces from the public dataset collected by Phillips
et al. [29]. Frontal face portraits of subjects with a neutral or happy
emotion are used as the real face images in our experiments.

We used a publicly-available dataset collected by Phillips et al. [29] as our
source of real faces from which multiple frontal face images with different
expressions were collected. The subjects varied in gender, age and ethnicity.
Some sample faces are shown in Fig 2. We used a subset of 1,023 images as a
pool of real faces (RPool1) for our experiments. The ethnicity and gender
distributions for this subset are shown in Fig. 3. Although the majority of
the subjects are White and Asian, a diversity of ethnicity is reflected in the
subjects of our subset. The gender distribution is also close to being
balanced. It is not exactly balanced because of the image quality requirements
of the algorithms (some images have to be filtered out because they are
unsuitable to use). The age of the subjects ranges from 18 years old to 69
years old.

Figure 3: Demographics of the subjects in the subset of the dataset of real
faces [29] used in our experiments. There is a diversity of ethnicity and
gender is close to balanced.

####  III-2 Synthetic faces generated by SREFI

SREFI [4] is a face synthesis approach based on a convolutional neural network
(CNN) face representation for associating similar face candidates, and it can
construct an arbitrarily large number of synthetic faces from real identities.
To synthesize a face, the input real face image is firstly triangularized into
region-specific triangles, while important face areas are prevented from being
placed in the corner of triangles. A set of “donor” faces (also real
identities) that are close to each other in the CNN feature representation
space are then selected. A VGG-Face CNN model [28] pre-trained on the VGG-Face
dataset [28] is used to calculate features. All donor faces are triangularized
like the base face.

Figure 4: Example of synthetic face generated using SREFI [4]. From left to
right: donor face, donor face, a synthetic face generated using the two donor
faces.

Synthetic faces are generated by stitching together the corresponding
triangles from the “donor” faces. To ensure the uniformity of the created
face, vital facial regions including the mouth, nose and eyes are restricted
so that they come from the same donor. A synthetic face is generated from a
donor set with a size of seven to ten real face images. In this work, we
created a synthetic face pool (SPool1) of 1,000 synthetic faces using SREFI.
The donor faces come from the same public dataset generating RPool1. Example
images generated using this method are shown in Fig. 4.

####  III-3 Synthetic faces generated by GANs

As GANs are very successful in generating high-quality synthetic face images,
there are multiple websites releasing the outputs of these networks to the
public for any use they see fit. We collected synthetic faces generated by
GANs by downloading images from the website thispersondoesnotexist.com.

All of the images in this set are generated using StyleGAN2 [17]. StyleGAN
[16] is a style-based generator that adjusts the style of each convolutional
layer’s output based on the input latent code, therefore directly controlling
the face synthesis process. Lower resolutions control high-level styles of the
output faces such as pose and face shape. A block of layers in the middle of
the generator controls the generation of facial features such as the eyes and
expression. Layers closer to the output layers control the fine details, such
as the color scheme. Using StyleGAN it is possible to generate high-resolution
(1024$\times$1024) synthetic face images with stochastic variation (e.g.,
freckles, hair). StyleGAN2 proposed certain modifications to further improve
the image quality of StyleGAN’s output. The improvements to the generator’s
architecture include weight demodulation, lazy regularization and path length
regularization. StyleGAN2 also revisits the progressive growth idea to
stabilize the training process. Overall, StyleGAN2 is able to generate images
with even finer details after all these changes. Some of the synthetic GAN-
generated faces that were collected are shown in Fig 5. The total number of
images in this synthetic face pool (SPool2) is 1,500.

Figure 5: Examples of synthetic faces generated using StyleGAN2 [17]. GAN
images used for our experiments are collected from thispersondoesnotexist.com.

A summary of the data sources is shown in Table I.

TABLE I: Sources of face images used in our experiments. | RPooL1 | SPooL1 | SPooL2
---|---|---|---
Number of images | 1,023 | 1,000 | 1,500

##  IV Human Perception Experiments

We conducted four independent experiments with human raters to examine the
plausibility of synthetic faces generated using different methods. The first
three experiments aim at investigating whether the synthetic faces will lead
to failure in people distinguishing between real and synthetic faces and how
environmental context affects judgement. The last experiment was designed to
shed light on the question of how much of the identity signal from real faces
used as input to a face generation approach leaks into synthetic faces.

A two-alternative forced-choice (2AFC) procedure is used in all four
experiments. This means the observer is presented with a pair of visual
stimuli (e.g., a pair of face images), and then has to select one of two
choices (e.g., “left” or “right”). Since answers outside of the provided two
options are not allowed, the observer’s choice is forced between the two
alternatives. The 2AFC design allows scientists to measure the statistics for
each visual stimulus pair across all observers and interpret the results.

Figure 6: Two sample pairs from Experiment 1. From left to right: synthetic
face in Pair 1, generated by SREFI; real face in Pair 1; synthetic face in
Pair 2, generated by StyleGAN2; real face in Pair 2. Figure 7: Human
performance on 1,000 pairs of real / synthetic face images with the background
visible. Each bar represents the number of pairs that has an accuracy larger
than the lower bound and smaller than or equal to the upper bound. For
example, the bar between 45% and 55% reflects that there are 237 pairs of
images that have an accuracy greater than 45% while being less than or equal
to 55%.

###  IV-A Experiments 1-3: Which face is real or synthetic?

####  IV-A1 Distinguishing faces with full context

In this experiment, we randomly selected 1,000 real face images from RPool1
and 500 synthetic face images from both SPool1 and from SPool2, which resulted
in 1,000 synthetic face images in total. Each real face was randomly paired
with a synthetic face, which resulted in 1,000 real / synthetic face image
pairs. All face images are pre-processed in the same manner. We first detect
face bounding boxes using Haar feature-based cascade classifiers. Then the
images are cropped with an aspect ratio of 3:4 at the center of the detected
face bounding box. Finally, we resize the cropped face images to
300$\times$400 in resolution, such as the samples shown in Fig. 6. Human
subjects are presented with one pair of face images side by side in each trial
and asked a question of “which of the faces is real (or synthetic)?”, where
the target question is varied randomly. Subjects are prompted to choose “left”
or “right” as an answer and the images stay on the screen until the choice has
been made.

Figure 8: Two sample pairs from Experiment 2. From left to right: synthetic
face in Pair 1, generated by SREFI; real face in Pair 1; synthetic face in
Pair 2, generated by StyleGAN; real face in Pair 2.

There are 176 human raters involved in this experiment, which was crowdsourced
on Amazon’s Mechanical Turk Service. For experiments 1-3, workers were paid $5
to rate 50 pairs of images. For each real / synthetic face image pair, at
least 10 workers are assigned to answer the question. On average 10.75 people
viewed each pair of images. We calculated the accuracy for each pair and the
distribution across pairs is shown in Fig. 7. Here accuracy is the number of
correct responses (e.g., “left” was selected when the real face image was
placed on the left and the subject was asked “which image is real?”) divided
by the total number of responses collected for this specific pair.

Considering Fig. 7, the data appear to be normally distributed. This brings
into question the human ability to accurately discriminate between real and
synthetic faces. To investigate this, we performed a one-tailed one sample
t-test on these data. The null hypothesis states that human raters are no
better than chance at identifying synthetic faces: $H_{0}:Acc_{human}=0.5$.
Two alternative hypotheses can also be formulated in the one-tailed scenario.
The alternative hypothesis considering the left tail states that human raters
tend to mistake synthetic faces as real faces (worse than chance):
$H_{a_{1}}:Acc_{human}<0.5$. The alternative hypothesis considering the right
tail states that human raters tend to identify synthetic faces correctly
(greater than chance): $H_{a_{2}}:Acc_{human}>0.5$.

As shown in Table III, for the test assessing the right tail, the t-statistic
is -1.834 and $p$ is greater than 0.05 (the 95% confidence interval). Thus the
null hypothesis is not rejected. Given the distribution in Fig. 7, this isn’t
a surprising finding. What is more interesting is that for the left tail, $p$
is less than 0.05, meaning the null hypothesis is rejected: people tend to
mistake synthetic faces as real faces in this experiment. Why might that be
the case?

There are a few possibilities as to why this occurs. The first two are related
to the facial features: the region of skin just below the eyes and the
horizontal line-up of the eyes with the frame of the face. Many of the
synthetic images have quite smooth skin under the eyes, and the eyes are lined
up on the exact horizontal. In essence, the synthetic faces, with smooth skin
and good symmetry, may look more pleasing to human observers. There is also
the possibility that the background is attracting undue attention, causing
people to make incorrect decisions based on irrelevant information. The next
experiment, which removes the backgrounds, will help us assess these
possibilities.

TABLE II: Statistics for Results of Experiments 1-4. | Mean | Std. | One-tailed One Sample t-test
---|---|---|---
| t-stat | p-value (left) | p-value (right)
Exp. 1 | 0.491 | 0.161 | -1.834 | 0.033 | 0.967
Exp. 2 | 0.497 | 0.160 | -0.675 | 0.250 | 0.750
Exp. 3 | 0.497 | 0.163 | -0.626 | 0.266 | 0.734
Exp. 4 | 0.501 | 0.183 | 0.133 | 0.553 | 0.447
TABLE III: Statistics for Results of Experiments 1-4. | Mean | Std.
---|---|---
Real Same Identities | 0.503 | 0.191
Real Different Identities | 0.522 | 0.166
Real SREFI Faces | 0.491 | 0.185
Real StyleGAN Faces | 0.507 | 0.173
Overall | 0.501 | 0.133

####  IV-A2 Distinguishing faces with only the face region visible

To examine whether the background information (e.g. scene, hair, clothes, and
pose) helps people recognize a synthetic face or interferes with their
judgement, we conducted a second experiment using Mechanical Turk. 1,000 real
/ synthetic face pairs were generated as in the previous experiment. However,
in the pre-processing step, images are further segmented to only keep the face
region. The segmentation is performed by using a pre-trained 81 landmark
facial alignment model offered by Dlib [18], which made use of Histogram of
Oriented Gradients (HOG) features combined with a linear classifier. Important
face components including the forehead are preserved. Some sample images are
shown in Fig. 8. As in Experiment 1, raters are asked “which face in the pair
is real (or synthetic)?” and images are left on the screen until the choice
has been made.

Figure 9: Human performance on 1,000 pairs of real / synthetic face images
with only the face area visible.

For each real / synthetic pair of images, we assigned at least 10 people to
answer the question. We collected responses for 10,550 pairs, and 174
different human raters were involved. Each pair of face images has an average
of 10.55 rater responses. The accuracy distribution is shown in Fig. 9, which
once again appears to be normally distributed.

We performed a one-tailed one sample t-test on the result as in Experiment 1,
where the hypotheses remained the same. As shown in Table III, the p-values
indicate that the null hypothesis cannot be rejected at the 95% confidence
interval. Therefore human performance is not better than randomly guessing
when recognizing synthetic faces by just the face region. Combined with the
results from Experiment 1, this suggests that synthetic faces have a higher
chance of fooling people with a background. Some people are likely relying on
details (e.g. hair, clothes, and scene) outside the face region in determining
whether a face is synthetic or real, meaning smooth skin and symmetry are not
significant clues. Current face synthesis techniques appear to be quite good
at producing realistic contextual details in these images.

Figure 10: Examples of samples from Experiment 3. From left to right: a face
image, adding light source from left, adding light source from top, adding
light source from right.

Figure 11: Two sample pairs from Experiment 3. From left to right: synthetic
face in Pair 1, generated by SREFI; real face in Pair 1; synthetic face in
Pair 2, generated by StyleGAN; real face in Pair 2. Figure 12: Human
performance on 1,000 pairs of real / synthetic face images with lighting
perturbation.

####  IV-A3 Distinguishing faces under varied lighting

We then moved on to examining whether light disrupts people’s judgement. In
this experiment, we evaluated the human ability to distinguish between real
and synthetic faces under a changing light source. 1,000 real/synthetic faces
were selected and pre-processed in the same manner as the first experiment. We
then added an additional light source for each face image using a 3D rendering
engine [7] to create a realistic sunlight effect. The direction of light is
randomly selected among “left”, “top” and “right”, as shown in Fig. 10. Sample
face pairs are shown in Fig. 11. The human raters are asked the same questions
as in the previous two experiments and the face images stay on the screen
until human raters answer the question.

In total, 10,350 real / synthetic image pairs were evaluated in this
experiment, and we ensured at least 10 human raters on Amazon’s Mechanical
Turk service examined each pair as in previous experiments. There were 172
different subjects involved in the evaluation and on average 10.35 people
examined each pair of images. We also calculated the accuracy for all image
pairs and the distribution of samples is shown in Fig. 12. Similar to the
previous experiments, the data appear to be normally distributed, suggesting
people are prone to mistaking synthetic faces for real faces. The one-tailed
one sample t-test results (Table III) has high values of $p$ for Experiment 3,
leaving the null hypothesis un-rejected.

This result, combined with those from the previous two experiments, gives us a
good snapshot of the current state of face synthesis technology. It is likely
that human observers cannot gather enough information to solve this task in
general. This finding has serious implications for the deployment of synthetic
faces in many applications. We can safely say that the technology has
developed to the point of being able to fool people in a reliable manner.

###  IV-B Experiment 4: Do synthetic faces give away any information about
real identities?

Different from the previous experiments, in this experiment we focused on the
question of the extent to which synthetic faces leak identity information
related to any real faces used by the generation algorithms. This is another
aspect of plausibility — if synthetic faces look too much like the real faces
they are generated from, they may be recognized as being real when technically
not. Most face synthesis algorithms generate faces based on one or more real
input faces.

For example, SREFI generates synthetic images based on a primary face and 7-10
“donor” faces proximal to it in the CNN feature space. Therefore the output
image preserves a portion of the features from the primary face, which brings
the risk of identity leakage. For StyleGAN, potential identity leakage is also
a concern. Given latent representations of multiple face images from training,
one way of creating a synthetic face image using StyleGAN is by using the
average of the latent representations as the new face’s latent representation
and generating an image from it. The latent representation of a face image
captures the style information from coarse (e.g., face shape, pose) to fine
(e.g., background color). Averaging the latent representation carries with it
the risk of creating a new latent vector that highly resembles one of its
components in some dimensions, which could lead to facial features that reveal
one of the original faces the new faces was generated from.

####  IV-B1 Data preparation

In this experiment, we prepared four types of image pairs described as
follows.

500 same-real identity image pairs are selected from the dataset that
generated RPool1 in Section III. We randomly selected 500 identities, each
with two different frontal images of a neutral or happy expression. 500
different-real identity image pairs are generated by randomly selecting two
identities from RPool1, with each identity contributing one frontal image to
the pair. To avoid obviously easy tasks, all the pairs are of the same gender.
We then used the same-real identities dataset to generate 500 real-synthetic
(SREFI) identity image pairs. 500 different real identities are selected as
the base faces. For each real identity, 7 to 10 identities from the rest of
the dataset are selected as donor faces to synthesize a new identity’s face
image [4]. Then the base face and the synthesized face constitute one real-
synthetic pair.

To create the 500 real-synthetic (StyleGAN) identity image pairs, we first
randomly selected 500 real face images from RPool1 and calculated the
corresponding latent representations using a pre-trained StyleGAN generator on
the FFHQ dataset [16]. For each real face image, $N$-1 other real faces are
randomly selected from the rest of the 500 images set. Following Equation 1,
the latent representation of the synthetic face to be created is the weighted
combination of the latent representations $L_{i}$ of the $N$ real faces. Then
the pre-trained StyleGAN generator is able to generate a face image from noise
based on the latent representation. An example of a StyleGAN synthesized face
is shown in Fig. 13. We set the variable $N$ to 4, and the coefficient
$\alpha$ is randomly selected to be between 0.2 and 0.3. Thus each real face
contributes equally to the output synthetic face while preserving certain face
features. One of the base faces and the synthesized face constitute one real-
synthetic pair.

$L_{output}=\sum_{i=1}^{N}\alpha\times{L_{i}}$ (1)

Fig. 14 shows samples of the four types of image pairs.

Figure 13: Face image of a synthetic identity generated by StyleGAN. The
output image on the right is generated by StyleGAN using the weighted average
of the latent representations of the four real faces on the left.

(a) P1 real face

(b) P2 real face

(c) P3 real face

(d) P4 real face

(e) P1 real face from the same identity

(f) P2 real face from a different identity

(g) P3 face generatd with SREFI

(h) P4 face generated with StyleGAN

Figure 14: Four sample pairs from Experiment 4. Top row: real face images in
Pairs 1-4. Bottom row (from left to right): another real face image from the
same identity as the face in (a), real face image from an identity different
from (b), synthetic face generated by SREFI using the real face (c) as an
input face, synthetic face generated by StyleGAN with the real face (d) as one
of the input faces.

####  IV-B2 Experiment

Figure 15: Results of Experiment 4. Human performance on 500 same-real
identities, 500 different-real identities, 500 real-SREFI identities, and 500
real-StyleGAN identities.

Participants are presented with a pair of images and prompted with a question
asking “Are these faces of the same person or are they of different people?”
They are forced to choose between “Same” and “Different”. Each participant
sees a block of 150 image pairs (50 images for each type of pairs), and a
total of 66 workers on Mechanical Turk participated in this task. Each pair of
images was evaluated by 8.53 people on average. The distribution of pairs with
respect to the probability of being considered from the same identity is shown
in Fig. 15.

For the real faces from the same identities, participants have a probability
of 0.503 ($\pm 0.191$) of determining if the two images are from the same
person. For the real faces of different identities, the probability of them
being identified as the same person is 0.522 ($\pm 0.166$). The same
probability value for the real-synthetic SREFI pairs is 0.491($\pm 0.185$) and
0.507($\pm 0.173$) for the real-synthetic StyleGAN pairs. These results are
surprising because there is no discernible difference between the real image
pairs and the real-synthetic (SREFI or STYLEGAN) pairs. However, referring
back to the literature on facial perception, it is well known that that
unfamiliar face recognition is significantly more difficult than familiar face
recognition — even more so when no training period is given [15]. This is a
plausible explanation, which bolsters the case for the effectiveness of
synthetic faces.

##  V DISCUSSION

The first three human perception experiments that were conducted in this study
showed that the human ability to accurately distinguish between synthetic and
real faces is not better than chance. What implication does this have for
social media and other applications on the Internet, and what can we do to
mitigate the negative impact of synthetic faces? This type of content has
already appeared on social networks like LinkedIn [33] in attempts to develop
relationships with users who may possess sensitive information, while evading
detection through reverse image searches. The next logical place for them to
appear is in romance scams conducted via dating apps, where low-level criminal
activity is already present [35]. If a scammer is using high quality synthetic
faces, our results indicate that it is probable that the average user will not
notice that the persona they are interacting with isn’t real based on the
profile photo. All is not lost, however. Proposals to add fingerprints to
synthetically generated content via the tools used to create it exist [38],
and effective detectors are being developed by the media forensics community
[36]. The latter is likely our best hope in assisting people in identifying
such content in the wild.

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