Diffusion-Inspired Dynamic Models for Enhanced Fake Face Detection

Mamadou Dian Bah

, Rajjeshwar Ganguly

and Mohamed Dahmane

Computer Research Institute of Montreal (CRIM), Canada

Keywords:

DeepFake Detection, Dynamic Model, Media Forensics, U-Net.

Abstract:

The conventional convolutional U-Net model was originally designed to segment images. Enhanced versions

of the architecture with timestep embedding and self-attention inspired by transformers were proposed in

the literature for image classiﬁcation tasks. In this paper, we investigate a U-Net–encoder architecture for

deepfake detection that involves two key features, the use of self-attention blocks to capture both local and

global content representation, and the integration of timestep embedding to capture the dynamic perturbation

of the input data. The model is trained and evaluated on FF++ dataset, comprising of real and deepfake syn-

thesized videos. Notably, compared to traditional models pretrained on ImageNet, our model demonstrates

superior performance. The experimental results highlight the effectiveness of our approach in achieving im-

proved classiﬁcation results for the challenging task of distinguishing real and deepfake images. The achieved

performances suggest that the model aims to leverage both spatial information and dynamic perturbation for

improved detection performance.

1 INTRODUCTION

The remarkable progress in artiﬁcial neural network

(ANN)-based technologies and particularly in genera-

tive AI has played a crucial role in manipulating mul-

timedia content. These advancements have made it

increasingly feasible to generate highly realistic syn-

thetic images, surpassing previous capabilities. In

recent years, generative models have witnessed re-

markable advancements in their ability to generate

human-like natural language (Brown et al., 2020),

high-quality synthetic images (Karras et al., 2020),

and diverse human speech and music. These models

ﬁnd utility in various domains, such as image gen-

eration from text prompts and feature representation

learning.

As a result, numerous captivating applications

have emerged in the realms of entertainment and ed-

ucation. Through all this capability, the term deep-

fake has emerged and refers to multimedia content

generated or altered by artiﬁcial intelligence models

(Gomes et al., 2020; Lattas et al., 2020). Noteworthy

examples include FaceApp (FaceApp, 2023), a popu-

lar application that leverages an autoencoder-decoder

architecture to seamlessly swap faces between two

https://orcid.org/0000-0002-0733-8587

https://orcid.org/0009-0002-8893-7756

https://orcid.org/0000-0002-2670-1433

images. DeepFaceLab (DeepfakeVFX.com, 2023),

an enhanced framework speciﬁcally designed for

face-swapping, pushes the boundaries of deepfake

technology. Another notable application is Face

Swapping GAN (FSGAN) (Nirkin et al., 2019), an

improved version of deepfake that employs Genera-

tive Adversarial Networks (GANs) to reﬁne segmen-

tation masks, resulting in remarkably higher qual-

ity output videos. Additionally, several other tools

are employed for deepfake content generation. Dis-

coFaceGAN (Deng et al., 2020), based on Style-

GAN structure (Karras et al., 2019), generates syn-

thetic face images of virtual individuals with dis-

tinct characteristics, while FaceShifter enables high-

ﬁdelity face swapping. The recent emergence of a

new generation of models, the so–called Denoising

Diffusion Models (DDMs), has raised great concern

for the spread of fake data, as they proved capable of

generating even more realistic and convincing fakes

than their predecessors, Generative Adversarial Net-

works (GANs). Models like Stable Diffusion (Rom-

bach et al., 2022) and DALL-E 2 (Ramesh et al.,

2022) are some of the best image generators available

and are renowned state-of-the-art Diffusion Models

(DMs) that excel in text-to-image translation.

Given the signiﬁcant threats posed by deepfakes,

such as the spread of misinformation, damage to rep-

utation, and invasion of privacy, it is crucial to de-

532

Bah, M., Ganguly, R. and Dahmane, M.

Diffusion-Inspired Dynamic Models for Enhanced Fake Face Detection.

DOI: 10.5220/0012391300003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 4: VISAPP, pages

532-538

ISBN: 978-989-758-679-8; ISSN: 2184-4321

velop advanced technologies for detecting deepfake

content. Humans often struggle to identify manipu-

lated videos, particularly in terms of spatial aspects,

and lack the ability to pinpoint the speciﬁc locations

and techniques used for manipulation. This highlights

the necessity for high-quality algorithms capable of

detecting manipulated videos on a large scale.

In this paper, we tackle the problem of detect-

ing facial manipulation. In particular, we focus on

all the manipulation techniques reported in (Rossler

et al., 2019) (i.e., deepfakes, Face2Face, FaceSwap

and NeuralTextures). We consider exploring a U-

Net–encoder architecture for deepfake detection that

involves two key features, the use of self-attention

blocks to capture both local and global spatial infor-

mation, and the integration of a predicted timestep

embedding to capture the dynamic perturbation of the

input data.

The rest of the paper is organized as follows. Sec-

tion 2 provides a literature review of commonly used

algorithms for deepfake detection. Section 3 presents

details on the proposed approach. Section 4 provides

the results and discussions. Finally, Section 5 con-

cludes with implications, limitations, and suggestions

for future research.

2 RELATED WORKS

Deep learning techniques have emerged as the domi-

nant approach for deepfake detection, as evidenced by

a comprehensive analysis of 122 studies conducted by

Rana et al. (Rana et al., 2022). Approximately 77% of

these studies employed deep learning models, specif-

ically Convolutional Neural Networks (CNNs) (Tariq

et al., 2018) and Recurrent Neural Networks (RNNs)

mostly used for fake videos detection. These mod-

els have shown great promise in effectively detecting

deepfake content.

In (Afchar et al., 2018) MeSoNet is a CNN archi-

tecture which is used to detect Face2Face and deep-

fakes manipulations. XceptionNet which uses depth-

wise separable convolutional layers with residual con-

nections (Chollet, 2017) has given the best result

in (Rossler et al., 2019) in Faceforensics++ (FF++)

dataset and is the most used baseline.

Although the use of deeplearning different ap-

proach is used based on the knowledge of human

face. Authors in (Haliassos et al., 2021) proposes a

method of detecting high-level semantic anomalies in

mouth motion, leveraging the hypothesis that most

video generators display a degree of high-level se-

mantic irregularities near the mouth. In (Zhao et al.,

2021a; Zhao et al., 2021b), ﬁne-grained classiﬁcation

is applied to distinguish subtle differences in visual

appearance and patterns. The authors propose Fake-

Buster in (Hubens et al., 2021) to address the issue

of detecting face modiﬁcation in video sequences us-

ing recent facial manipulation techniques. In (Ismail

et al., 2021), the YOLO face detector is used to ex-

tract the face area from video frames, while the In-

ceptionResNetV2 CNN is utilized to extract features

from these faces.

However detecting deepfakes in videos solely

based on counterfeit images can be difﬁcult due to

the temporal features of videos and variation in frame

resolution. In (Ranjan et al., 2020), the CNN-LSTM

combo is used to identify and classify the videos as

fake or real.

FSSPOTTER (Chen et al., 2020), for instance,

uses spatial and temporal clues to detect swapped

faces in videos. These features are fed into the XG-

Boost, which works as a recognizer on the top level

of the CNN network. Physiological signals are also

used for deepfake detection. DFT-MF (Elhassan et al.,

2022) is a deep-fake detection model that uses deep

learning approaches to detect deepfake videos by iso-

lating, analyzing, and verifying lip/mouth movement.

Eye blinking based signal detection is also used to

determine if a video is real or a deepfake generated.

Deep Vision detects deepfake videos by focusing on

eye blink patterns. FakeCatcher (Ciftci et al., 2020)

is a method that addresses the challenge of detecting

deepfakes by exploiting the fact that biological sig-

nals obtained from facial regions are not positionally

and temporally well-preserved in synthetic content of

portrait videos.

In a recent study, U-Net has garnered signiﬁcant

attention due to its computational and efﬁciency ad-

vantages in segmentation and feature extraction (Ron-

neberger et al., 2015). Eff-YNet, introduced in (Tjon

et al., 2021), is a noteworthy example of the syn-

ergy between an EfﬁcientNet encoder and a U-Net

structure. This fusion enables the model to effec-

tively perform both classiﬁcation and segmentation

tasks on deepfake videos. Similarly, the work pre-

sented in (Bhilare et al., 2022) aligns with this theme,

where authors introduce U-YNet. This model in-

tegrates segmentation and classiﬁcation capabilities

by utilizing a U-Net Encoder and Decoder to gener-

ate segmentation maps, with a classiﬁcation branch

seamlessly integrated at the end of the U-Net Encoder.

These innovations highlight the versatility and com-

putational efﬁciency of U-Net in addressing a wide

array of computer vision challenges. Moreover, in the

recent approach for deepfake generation U-Net is one

of the most important components of diffusion model

(Dhariwal and Nichol, 2021) because it facilitates the

Diffusion-Inspired Dynamic Models for Enhanced Fake Face Detection

533

Figure 1: Architecture of proposed method. This model is refered as ClOneSelect.

actual diffusion process and improves sample qual-

ity for denoising score matching (Jolicoeur-Martineau

et al., 2020). In (Dhariwal and Nichol, 2021), the au-

thors found that a pre-trained diffusion model can be

conditioned using the gradients of a classiﬁer, further

underscoring the signiﬁcance of U-Net as classiﬁer in

guiding and improving the diffusion process for im-

age generation tasks. In our research, we investigate

the use of a U-Net encoder in conjunction with tech-

niques derived from the diffusion approach to detect

deepfake generated faces. Results is compared to the

traditional method such as Xception (Rossler et al.,

2019) and EfﬁcienceNet (Bonettini et al., 2021) used

in other works.

3 METHOD

The model consists of 3 blocks: one block containing

a layer that predicts the timestep t of the input image,

a block that adds noise ε to the image x

based on

the timestep, an encoder that takes an image as input

and provides vector of 1000 values, and ﬁnally, the

classiﬁer layer which maps 1000 to 1. In Figure 1, we

present the architecture of our method.

3.1 Timestep Predictor

The forward process in a diffusion model works by

gradually adding noise to an initial clean or observed

image to generate a sequence of increasingly noisier

images. This process simulates the generation of sam-

ples in a probabilistic model where the ﬁnal sample

represents the output of interest.

Diffusion models generate samples by reversing a

gradual process of introducing noise. Essentially, the

sampling process begins with noisy x

and progres-

sively creates less noisy samples x

T −1

, x

T −2

, ... until

reaching a ﬁnal sample x

. Each timestep, denoted

as t, corresponds to a speciﬁc noise level, and x

can

be viewed as a combination of a signal x

with some

noise ε, where the signal-to-noise ratio is determined

by the timestep t. In Figure 2, a sample of images is

shown at different timestep using a linear scheduler

for adding noise.

The authors of guided diffusion (Radford et al.,

2021) demonstrated the effectiveness of incorporat-

ing a projection of the timestep embedding into each

residual block of the U-Net encoder. This addition

serves to guide the diffusion model towards the de-

sired class, effectively transforming the architecture

as if we have multiple models in one. This adaptation

occurs in accordance with the timestep, allowing for

dynamic adjustments in classiﬁcation.

In our context, to take advantage of all those mod-

els, we have incorporated a Linear layer that takes the

input of 1000 possible timesteps and selects a single

value t, representing the most suitable timestep for

improved classiﬁcation. This value t represents the

impact of the noise ε to be applied to the image x.

3.2 U-Net Encoder

The UNet model uses a stack of residual layers and

downsampling convolutions. In addition, they use a

global attention layer at different 32×32, 16×16 and

8x8 resolutions with a single head, and add a pro-

jection of the timestep embedding t into each resid-

ual block. Timestep t embedding capture the dy-

namic perturbations present in the input data. By

learning embeddings speciﬁc to each timestep, the

model gains the ability to make accurate predictions

regarding whether an image belongs to the ”fake” or

VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications

534

Figure 2: Example of images with corresponding timestep using a linear scheduler.

”real” class, taking into account the level of the intro-

duced noise. This integration empowers the model

to dynamically adapt its processing strategy, effec-

tively leveraging the unique characteristics and dy-

namic variations observed at different time points

within the input data. The model, denoted as C, using

the noisy images x

for t ∈ {0, ..., T }. Each timestep t

corresponds to a certain noise level where T refers to

the highest noise level. This encoder is trained to pre-

dict the class label of the input image x according to

the level t of the added noise. Where x

is the image

at timestep t = 0.

The output of the U-Net encoder architecture is

the downsampling trunk of the model with an atten-

tion pool at the 8x8 layer to produce the ﬁnal output

of 1000 which correspond to the thousand classes of

ImageNet. In our case, we added a classiﬁer layer to

map the 1000 to 1. The encoder can be represented as

in Equation 1.

ˆy = C(x

;θ

) (1)

where ˆy represents the predicted class label, x

is the

input image at timestep t, and θ

denotes the optimiz-

able parameters of the classiﬁer network.

4 EXPERIMENT AND RESULTS

To assess the effectiveness of our proposed approach,

we conducted extensive experiments on the FF++

dataset (Rossler et al., 2019). The FF++ dataset stands

out as a large-scale facial manipulation dataset cre-

ated through state-of-the-art video editing techniques.

In particular, it combines classical computer graph-

ics methods, such as Face2Face (Thies et al., 2016)

and FaceSwap, with learning-based strategies, includ-

ing deepfakes and NeuralTextures (Thies et al., 2019).

Each of these methods was applied to 1000 high-

quality pristine videos downloaded from YouTube,

Table 1: Performance Evaluation on FF++ Dataset. Meth-

ods labeled with ”ClOne” are proposed by us.

Method AUC

EfﬁcienceNet-B4 (Bonettini et al., 2021) 96

Xception (Rossler et al., 2019) 94.98

ClOne 95.26

ClOneMixXcept 95.72

ClOneMixB4 96.68

ClOneSelect 96.21

carefully selected to ensure that the subjects faces

were nearly frontal and free from occlusions. These

video sequences consist of at least 280 frames each.

Ultimately, this dataset comprises over 1.8 million

images obtained from 4000 manipulated videos.

To ensure a fair comparison, in our experiment we

adopted the evaluation protocol deﬁned in (Rossler

et al., 2019).

We used similar splits, selecting 720 videos for

training, 140 for validation and 140 for test from the

pool of original sequences taken from YouTube. The

corresponding fake videos are assigned to the same

split. We primarily focus on the subject face region

for analysis. We use the BlazeFace extractor for pre-

processing, extracting the best-conﬁdence face from

each frame. Our network input image shape is 256 ×

256. During training and validation, we enhance the

model robustness with data augmentation, including

downscaling, ﬂipping, brightness, contrast, hue, sat-

uration adjustments, noise addition, and JPEG com-

pression.

A total of 230302 frames were extracted from the

dataset for training, 26879 frames were set aside for

validation and 26879 for testing purposes.

During experiments, we utilized the Adam opti-

mizer with an initial learning rate of 0.0001 and cross-

entropy loss. Batch size of 32 is used and the model

is validated every 500 iteration on 6000 sample ran-

domly selected in validation set. Our model was ex-

posed to diverse and challenging conditions during

Diffusion-Inspired Dynamic Models for Enhanced Fake Face Detection

535

training, allowing it to learn and adapt to various types

and levels of noise commonly encountered in real-

world scenarios. To optimize the training process, we

performed ﬁne-tuning on the U-Net encoder, which

was initially pretrained on ImageNet with the speciﬁc

purpose of guiding the conditional image synthesis in

diffusion models (Radford et al., 2021).

To evaluate the performance of our proposed

method, we conducted various tests including com-

parisons with various models and cross-data vali-

dation. Our method was compared with Xception

(Rossler et al., 2019) and EfﬁcienceNet-B4 (Bonettini

et al., 2021), both employed in deepfake detection.

Furthermore, we conducted an ablation study of

the model. Initially, we removed the timestep predic-

tor and trained the model with only a timestep of 0.

In other words, the model was directly trained with

the original image as input, without adding noise dur-

ing the training process. We refer to this model as

ClOne throughout the paper. Additionally, we de-

veloped hybrid versions by combining Xception and

EfﬁcienceNet-B4 with the U-Net encoder model, re-

ferred to as ClOneMixXcept and ClOneMixB4, re-

spectively. Hybrid models performed feature extrac-

tion from the initial U-Net encoder blocks and used

them as input for Xception. Similar to ClOne, the im-

age was not corrupted by noise.

The results presented in Table 1 demonstrate that

ClOneMixB4 and ClOneSelect models present the

highest AUC values respectively 96.68 and 96.21.

ClOne also performs well, achieving an AUC of

95.26. EfﬁcienceNet-B4 gives the third best result an

AUC of 96 and ClOneMixXcept achieves an AUC of

95.72. These results underscore the performance of

the proposed models.

We performed cross-dataset evaluation on Celeb-

DF (V2) (Yuezun Li and Lyu, 2020), an extensive

dataset designed to mimic the visual quality of online

videos. Unlike its predecessor, Celeb-DF (V1), which

contained only 795 deepfake videos, this updated

version includes 590 original videos from YouTube,

spanning various ethnicities. Additionally, Celeb-

DF (V2) encompasses 5639 corresponding deepfake

videos, making it a valuable resource for evaluation

and analysis. For testing purposes, 16,565 frames

were selected from a subset of 518 designated as test

videos.

In Table 2, the results indicate that ClOne and

ClOneMixXcept, show better performance compared

to Xception. This suggests that the proposed ap-

proach is robust to detect manipulated content in the

Celeb-DF (V2) dataset. Although ClOneSelect per-

forms slightly below EfﬁcienceNet-B4 in terms of

AUC, it still demonstrates better performance than

Table 2: Cross-dataset evaluation on Celeb-DF (V2)

Dataset. Methods with ”ClOne” are proposed by us.

Method AUC

EfﬁcienceNet-B4 (Bonettini et al., 2021) 77.66

Xception (Rossler et al., 2019) 73.06

ClOne 75.69

ClOneMixXcept 75.69

ClOneMixB4 74.06

ClOneSelect 74.85

Xception and ClOneMixB4 in this dataset.

5 CONCLUSION

In this paper we showcased the remarkable perfor-

mance of the U-Net model with attention and timestep

embedding in distinguishing between real and deep-

fake images. By capturing both local and global in-

formation and considering the dynamic perturbation

of the input data, our model outperformed the well-

established deepfake detection methods Xception and

EfﬁcienceNet-B4 on the FF++ dataset in terms of

AUC.

As a future perspective, we intend to add a branch

of noise prediction from the input image in a multitask

learning setting. Additionally, we aim to explore the

utilization of other datasets to enhance the model’s

generalization capabilities.

ACKNOWLEDGEMENTS

The work was partially supported by a NSERC (Nat-

ural Sciences and Engineering Research Council of

Canada) Discovery Grant under grant agreement No

RGPIN-2020-05171.

We gratefully acknowledge the support of the Com-

puter Research Institute of Montreal (CRIM), the

Minist

ere de l’

Economie et de l’Innovation (MEI) of

Quebec, and The Natural Sciences and Engineering

Research Council of Canada (NSERC).

REFERENCES

Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I.

(2018). Mesonet: a compact facial video forgery de-

tection network. In 2018 IEEE international work-

shop on information forensics and security (WIFS),

pages 1–7. IEEE.

Bhilare, O., Singh, R., Paranjape, V., Chittupalli, S.,

Suratkar, S., and Kazi, F. (2022). Deepfake cli: Ac-

celerated deepfake detection using fpgas. In Interna-

VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications

536

tional Conference on Parallel and Distributed Com-

puting: Applications and Technologies, pages 45–56.

Springer.

Bonettini, N., Cannas, E. D., Mandelli, S., Bondi, L.,

Bestagini, P., and Tubaro, S. (2021). Video face ma-

nipulation detection through ensemble of cnns. In

2020 25th international conference on pattern recog-

nition (ICPR), pages 5012–5019. IEEE.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.,

Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G.,

Askell, A., Agarwal, S., Herbert-Voss, A., Krueger,

G., Henighan, T., Child, R., Ramesh, A., Ziegler,

D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler,

E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner,

C., McCandlish, S., Radford, A., Sutskever, I., and

Amodei, D. (2020). Language models are few-shot

learners.

Chen, P., Liu, J., Liang, T., Zhou, G., Gao, H., Dai, J.,

and Han, J. (2020). Fsspotter: Spotting face-swapped

video by spatial and temporal clues. In 2020 IEEE

International Conference on Multimedia and Expo

(ICME), pages 1–6.

Chollet, F. (2017). Xception: Deep learning with depthwise

separable convolutions. In Proceedings of the IEEE

conference on computer vision and pattern recogni-

tion, pages 1251–1258.

Ciftci, U. A., Demir, I., and Yin, L. (2020). Fakecatcher:

Detection of synthetic portrait videos using biological

signals. IEEE transactions on pattern analysis and

machine intelligence.

DeepfakeVFX.com (2023). DeepFaceLab - Deep-

fakeVFX.com — deepfakevfx.com. https://www.

deepfakevfx.com/downloads/deepfacelab/. [Accessed

21-Jun-2023].

Deng, Y., Yang, J., Chen, D., Wen, F., and Tong, X. (2020).

Disentangled and controllable face image generation

via 3d imitative-contrastive learning.

Dhariwal, P. and Nichol, A. (2021). Diffusion models beat

gans on image synthesis.

Elhassan, A., Al-Fawa’reh, M., Jafar, M. T., Ababneh, M.,

and Jafar, S. T. (2022). Dft-mf: Enhanced deepfake

detection using mouth movement and transfer learn-

ing. SoftwareX, 19:101115.

FaceApp (2023). FaceApp: Face Editor — faceapp.com.

https://www.faceapp.com/. [Accessed 21-Jun-2023].

Gomes, T. L., Martins, R., Ferreira, J., and Nascimento,

E. R. (2020). Do as i do: Transferring human mo-

tion and appearance between monocular videos with

spatial and temporal constraints.

Haliassos, A., Vougioukas, K., Petridis, S., and Pantic, M.

(2021). Lips don’t lie: A generalisable and robust ap-

proach to face forgery detection. In Proceedings of the

IEEE/CVF conference on computer vision and pattern

recognition, pages 5039–5049.

Hubens, N., Mancas, M., Gosselin, B., Preda, M., and Za-

haria, T. (2021). Fake-buster: A lightweight solution

for deepfake detection. In Applications of Digital Im-

age Processing XLIV, volume 11842, pages 146–154.

SPIE.

Ismail, A., Elpeltagy, M., S. Zaki, M., and Eldahshan,

K. (2021). A new deep learning-based methodology

for video deepfake detection using xgboost. Sensors,

21(16):5413.

Jolicoeur-Martineau, A., Pich

e-Taillefer, R., des Combes,

R. T., and Mitliagkas, I. (2020). Adversarial score

matching and improved sampling for image genera-

tion. CoRR, abs/2009.05475.

Karras, T., Laine, S., and Aila, T. (2019). A style-based

generator architecture for generative adversarial net-

works.

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J.,

and Aila, T. (2020). Analyzing and improving the im-

age quality of stylegan.

Lattas, A., Moschoglou, S., Gecer, B., Ploumpis, S., Tri-

antafyllou, V., Ghosh, A., and Zafeiriou, S. (2020).

Avatarme: Realistically renderable 3d facial recon-

struction ”in-the-wild”.

Nirkin, Y., Keller, Y., and Hassner, T. (2019). Fsgan: Sub-

ject agnostic face swapping and reenactment.

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G.,

Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark,

J., et al. (2021). Learning transferable visual models

from natural language supervision. In International

conference on machine learning, pages 8748–8763.

PMLR.

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen,

M. (2022). Hierarchical text-conditional image gener-

ation with clip latents.

Rana, M. S., Nobi, M. N., Murali, B., and Sung, A. H.

(2022). Deepfake detection: A systematic literature

review. IEEE Access.

Ranjan, P., Patil, S., and Kazi, F. (2020). Improved general-

izability of deep-fakes detection using transfer learn-

ing based cnn framework. In 2020 3rd international

conference on information and computer technologies

(ICICT), pages 86–90. IEEE.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Om-

mer, B. (2022). High-resolution image synthesis with

latent diffusion models.

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-

net: Convolutional networks for biomedical image

segmentation. In Medical Image Computing and

Computer-Assisted Intervention–MICCAI 2015: 18th

International Conference, Munich, Germany, October

5-9, 2015, Proceedings, Part III 18, pages 234–241.

Springer.

Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies,

J., and Nießner, M. (2019). Faceforensics++: Learn-

ing to detect manipulated facial images. In Proceed-

ings of the IEEE/CVF international conference on

computer vision, pages 1–11.

Tariq, S., Lee, S., Kim, H., Shin, Y., and Woo, S. S. (2018).

Detecting both machine and human created fake face

images in the wild. In Proceedings of the 2nd interna-

tional workshop on multimedia privacy and security,

pages 81–87.

Thies, J., Zollh

ofer, M., and Nießner, M. (2019). Deferred

neural rendering: Image synthesis using neural tex-

Diffusion-Inspired Dynamic Models for Enhanced Fake Face Detection

537

tures. Acm Transactions on Graphics (TOG), 38(4):1–

12.

Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and

Nießner, M. (2016). Face2face: Real-time face cap-

ture and reenactment of rgb videos. In Proceedings of

the IEEE conference on computer vision and pattern

recognition, pages 2387–2395.

Tjon, E., Moh, M., and Moh, T.-S. (2021). Eff-ynet: A dual

task network for deepfake detection and segmenta-

tion. In 2021 15th International Conference on Ubiq-

uitous Information Management and Communication

(IMCOM), pages 1–8.

Yuezun Li, Xin Yang, P. S. H. Q. and Lyu, S. (2020). Celeb-

df: A large-scale challenging dataset for deepfake

forensics. In IEEE Conference on Computer Vision

and Patten Recognition (CVPR).

Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., and

Yu, N. (2021a). Multi-attentional deepfake detec-

tion. In Proceedings of the IEEE/CVF conference on

computer vision and pattern recognition, pages 2185–

2194.

Zhao, L., Zhang, M., Ding, H., and Cui, X. (2021b).

Mff-net: deepfake detection network based on multi-

feature fusion. Entropy, 23(12):1692.

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