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2023

STATE OFDEEPFAKES

Realities, Threats, and Impact

OVERVIEW OF CURRENT STATE98%

Deepfake pornography
makes up 98% of all
deepfake videos online

7

Total video views across top 10 dedicated deepfake porn websites

303,640,207

Deepfake porn

OUT OFTHE TOP10

pornography websites
host deepfakes

Total traffic across top 10 dedicated deepfake porn websites

34,836,914

The majority of deepfake videos online
are related to pornography, while othernon-pornographic types of deepfakes
have also become more popular.

Total deepfake videos online in 2023

Number of deepfake porn

95,820

3,725
2022

550%

Dedicated deepfake pornography

21,019
2023

464%

2023

Mainstream pornography website

90%
10%GENDER

2019

Deepfake pornography videos by host website type

TARGETED INDIVIDUALS

99% of the individuals targeted in deepfake pornography are women.

DEEPFAKE PORNOGRAPHY
1South Korea

2America

53%
20%

3

Japan

10%

4

England

6%

5

China

3%

6India7

Taiwan2%2%8Israel1%9Other

4%

South Korean Singers and Actresses constitute 53% of the individuals featured in deepfake pornography and are the most commonly targeted group.

94% of those featured in deepfake pornography

videos work in the entertainment industry
Profession

Susceptibility Rate to Deepfake Pornography

Singer58%

Actress

33%

Social Media Influencer

3%

Model2%

Athlete2%

Other2%

Top 10 individuals most frequently targeted by deepfake pornography
Video Features

Views

South Korean Singer

1,595

5,611,500

South Korean Singer

1,238

3,865,000

South Korean Singer

923

2,305,500

South Korean Singer

844

3,395,500

South Korean Singer

803

1,365,200

South Korean Singer

761

3,294,000

South Korean Singer751

1,995,800

Thai Singer

733

3,852,000

South Korean Singer

733

3,084,000

British Actress713

3,190,100

Nationality & Profession

Due to the sensitive nature of the data, we have opted not to disclose the names of these individuals. Instead, we have provided information about their nationality and profession.

Advancements in Deepfake Technology
The rise of GANs
At the heart of the deepfake revolution lies the emergence and refinement of Generative Adversarial Networks (GANs). 

Introduced in 2014, these neural network architectures have since become the driving force behind realistic data generation, including images, audio, andtext used in deepfake technology. As a result, they have led to a wealth of academic research and publications. Scholars and researchers worldwide haverecognized the potential and versatility of GANs, resulting in a substantial body of work exploring various aspects of these neural network models. 

The table below summarizes the extent of academic research conducted in this field from 2014 to 2021.

6 58621582

47 1246

142014

2015

36
2016

2017

2018

2019

2020

2021

Number of academic report about GANs published per yearIn 2014, GANs made their academic debut with just one research publication. This seminal paper by Ian Goodfellow and his collaborators introduced theconcept of GANs, outlining their novel architecture and adversarial training framework.

In the year 2021, the field of GAN research will reach a remarkable milestone. A staggering 658 research publications focused on GANs emerged, marking
an extraordinary increase compared to the humble beginnings of just a single publication in 2014. 

The exponential growth in GAN-related research is a testament to the versatility and potential of this approach.

60%

40%

Web-based

Downloadabletoolstools

15 deepfake creation community websites and forums with 609,464 non-unique members

Most creation communities and forums were located on deepfake pornographic websites.

It now takes less than 25 minutes and costs $0 to create a 60-second
deepfake pornographic video of anyone using just one clear face image.

We surveyed 1522 American men regarding
deepfake pornography and foundA significant 57%

of respondents
expressed their interestin deepfake technology,
driven by a curiosity toexplore the capabilitiesof this emerging field.

74% of deepfake pornographyusers don't feel guilty about it

Celebrity attraction
ranked second at 48%,
indicating that many
are drawn to deepfake
content featuring theirfavorite stars.

Fantasy fulfillment,identified by 36% ofparticipants, reflects adesire for personalized
and tailoredexperiences within
adult entertainment.

Top 4 reasons why they don't feel guilty about it are
I know it's not the person.

36%

I don't think it hurts anyone as long as it's only for my personal interest.

30%

Deepfake porn is simply a more realistic version of sexual imagination.

For me, deepfake porn is not much different from regular porn.

28%

One out of five daily porn users has
swapped regular porn for deepfake porn
Remarkably, one in five daily pornography users revealed thatdeepfake content has replaced traditional pornography in theirviewing preferences. This finding shows the impact ofdeepfake technology on the adult entertainment industry andits appeal to a substantial portion of the audience.

29%

57% 48% 36%
73%

of respondents expressed
the desire to report such
incidents to the authorities.

68%

indicated that they would feelshocked and outraged by theviolation of someone's privacy
and consent in the creation ofdeepfake pornographic content.

When we asked, "If someone close to you became a victim of deepfake porn,what is your reaction?"

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