Deep Fakes Artificial Intelligence.pptx

T H E D A R K S I D E O F
A R T I F I C I A L I N T E L L I G E N
DEEP-FAKE
N I L A Y D E S H M U K H

What are Deep Fakes
• Deepfakes are a form of synthetic media that use artificial
intelligence and machine learning algorithms to create fake
images, videos, or audio recordings that appear to be real. They
are created by manipulating or combining existing content to
produce a realistic result.
• The term "deepfake" comes from the underlying technology
such as deep learning algorithms which teach themselves to
solve problems with large sets of data and can be used to create
fake content of real people.

History of Deep Fake
19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
19th century
Photo
manipulation was
developed

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
20th century
Technology steadily
improved.

1990 2014 2017 2018 2019 2020 2021 2022
19th
century
20th
century

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
1990
Deepfake technology
has been developed
by researchers at
academic institutions
Technology steadily
improved.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2014
The first academic
research paper on
deepfake tech.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2017
Deepfakes gained
widespread
attention on Reddit.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2018
The term "deepfake"
became widely used after a
user named "Deepfakes"
posted several videos
featuring the faces of
celebrities.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2018
In the same year,
researchers and tech
companies started
developing tools to detect
deepfake content.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2019
Advanced, with new
techniques for manipulating
not only faces but also voices
and even entire bodies.
In the same year, the US
Congress held hearings on
deepfakes.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2020
Deepfake technology
continued to evolve.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2021
Deepfakes remained a
concern, with several
incidents of deepfake
videos being used to
spread disinformation.

19th
century
20th
century 1990 2014 2017 2018 2019 2020 2021 2022
2022
Lots of open-source
software are
developed to create
deep fake easily

Point to be Noted
1.How Is A Deepfake Different From
Photoshop or Face swap?
Fake images show up all over the internet these days and are
often harmless. You’re probably familiar with the amusing
effects of “face swapping” on snapchat or other photo apps,
where you can put someone else’s face on your own and
vice versa. Or maybe you participated in the “age yourself”
trend and ran your face through a fake aging app that
showed you what you might look like in your ripe old age.

Point to be Noted
2. So what’s the big deal?
In today’s society, the vast majority of people get their
information about the world and formulate opinions based
on content from the internet. Therefore, anyone with the
capability to create deepfakes can release misinformation
and influence the masses to behave in a way that will
advance the faker’s personal agenda in some way. Deepfake-
based misinformation could produce damage on a micro and
macro scale.

Deepfakes Matter Because
Believability
If we see and hear something with our own eyes and ears,
we believe it to exist or to be true, even if it is unlikely.
The brain's visual system can be targeted for misperception,
in the same way optical illusions trick our brains.

Deepfakes Matter Because
Accessibility
The technology of today and tomorrow, will allow all of us to
create fakes that appear real, without a significant
investment in training, data collection, hardware and
software.
However, the accessibility of deepfake technology also
means that it can be used for both good and bad purposes.

How Deep Fake Works
• The main technology for creating deepfakes is deep learning,
a machine learning method used to train deep neural
networks (DNNs).
• DNNs consist of a large set of interconnected artificial
neurons, referred to as units.
• Much like neurons in the brain, while each unit itself
performs a rather simple computation, all units together can
perform complex nonlinear operations.
• In case of deepfakes, this is mapping from an image of one
person to another.

How Deep Fake Works
• Deepfakes are commonly created using a specific deep
network architecture known as autoencoder.
• Autoencoders are trained to recognize key characteristics of
an input image to subsequently recreate it as their output. In
this process, the network performs heavy data compression.
• Autoencoders consist of three subparts:
• an encoder (recognizing key features of an input face)
• a latent space (representing the face as a compressed
version)
• a decoder (reconstructing the input image with all
detail)

How Deep Fake Works
Autoencoder: a DNN architecture commonly used for generating deepfakes.

How Deep Fake Works
Encoder
• Much like an artist drawing an image, the encoder
compresses an image, from originally tens of
thousands of pixels into a few hundred (typically
around 300) measurements.
• These measurements can relate to particular facial
characteristics e.g., eyes are open or closed, the
head pose, the emotional expression, skin color,
etc.

How Deep Fake Works
Latent space
• Represents different facial aspects of the person on
which it is trained.
• It is often compared to information bottlenecks so that
the network can learn more general facial
characteristics rather than memorizing all input
examples of specific people.
• The compression achieved by the encoding of an input
image into the latent space can be 0.1% of the memory
needed to store the original input image.

How Deep Fake Works
Decoder
• Decompresses the information in the latent space to
reconstruct an image as perfectly as possible.
• The performance of the whole autoencoder network
is measured by how much the input and generated
(output) images resemble each other. This task is
made difficult because of the heavy data
compression performed by the encoder.

How Deep Fake Works
The Deep Fake Trick
• Two separate autoencoders trained each on a different
person will be very different and cannot be integrated.
• The trick for creating deepfakes lies in sharing the
encoder across two networks such that they remain
compatible.
• This way, the image of one person can be used to
compute a compressed latent space representation,
from where the decoder of another person is used to
create the fake.

How Deep Fake Works
The Deep Fake Trick
• Using the same encoder and hence latent space representation for images of two separate people is
key to understanding deepfakes.
• If two autoencoders were trained separately, the latent spaces would not be aligned (Brie, and Carrey
latent space below). Encoder sharing will result in an aligned latent space (grey dots). The
autoencoders can then be used to match from one to another person.

How Deep Fake Works
A shared encoder is key to creating novel facial images of a target person that exhibit the same
emotional expression, head posture, etc. as the original facial characteristics. This new image can then be
doctored back into the original image to create a fake scene.

How Deep Fake Works
Additional Technology required to develop Deep
Fakes
• GAN neural network technology is used in the development of
all deepfake content, using generator and discriminator
algorithms.
• Convolutional neural networks analyze patterns in visual
data. CNNs are used for facial recognition and movement
tracking.
• Natural language processing is used to create deepfake
audio. NLP algorithms analyze the attributes of a target's
speech and then generate original text using those attributes.
• High-performance computing is a type of computing that
provides the significant necessary computing power required
by deepfakes.

Applications of Deep Fake
Entertainment
Deepfakes can be used to
create entertaining
content, such as videos or
images that place a
person in a different time
or setting. For example,
deepfakes have been
used to create mashups
of celebrities in movies or
TV shows that they never
actually appeared in.

Entertainment
create entertaining
images that place a
deepfakes have been
Advertising and
Marketing
Deepfakes can be used
to create compelling
marketing content or
advertisements. For
example, a company
could create a deepfake
of a celebrity endorsing
their product, without
actually having to pay
the celebrity for their
endorsement.

Entertainment
create entertaining
images that place a
deepfakes have been
Advertising and
Marketing
advertisements. For
example, a company
endorsement.
Education and
Research
to create realistic
simulations for
educational or research
purposes. For example,
they could be used to
simulate historical events
or scientific phenomena.

Entertainment
create entertaining
images that place a
deepfakes have been
Advertising and
Marketing
advertisements. For
example, a company
endorsement.
Education and
Research
to create realistic
simulations for
educational or research
purposes. For example,
simulate historical events
or scientific phenomena.
Accessibility
to improve accessibility
for people with
disabilities. For example,
create sign language
interpretation of spoken
language or to create
more natural-sounding
synthesized speech for
people who use assistive
technology.

Companies using Deep Fake
Cognitivescale
This company uses deep
learning to create virtual
assistants and chatbots
that can interact with
customers in a more
natural and human-like
way.

Cognitivescale
customers in a more
way.
Modulate
Modulate is a company
that uses deep learning
to create synthetic voices
that can be used in
gaming and virtual reality
applications. Their
technology can also be
used to create more
natural-sounding voice
assistants

Cognitivescale
customers in a more
way.
Modulate
that can be used in
applications. Their
used to create more
assistants
Synthesia
Synthesia uses deep
learning to create videos
of people speaking
different languages or
with different accents.
Their technology can be
used to create more
engaging language
learning content or to
improve accessibility for
people with hearing
impairments.

Cognitivescale
customers in a more
way.
Modulate
that can be used in
applications. Their
used to create more
assistants
Synthesia
Synthesia uses deep
learning to create videos
of people speaking
different languages or
with different accents.
Their technology can be
used to create more
engaging language
learning content or to
improve accessibility for
people with hearing
impairments.
Pinch of AI
learning to create
personalized recipes
based on users' dietary
preferences and
restrictions. Their
used to analyze food
images and recommend
recipes based on the
ingredients.

Dark Side of Deep Fake
Why are they a problem?
• While deepfakes do have a number of beneficial
uses, including political satire, comedy,
entertainment, and education, a number of its
associated dangers are severe, even existential
threats. Since their introduction, deepfake
technology has been utilized extensively to Spread
misinformation and inspire misunderstanding, fear
or disgust. Create false narratives of people.
• The potential for disinformation is the biggest
concern with Deep Fake. It can be used to create
fake news, propaganda, and even blackmail. It can
also be used to manipulate public opinion,
influence elections, and damage reputations.

Dark Side of Deep Fake
Political Manipulation: Deepfakes can be used to manipulate political opinions or elections. For example, a
deepfake video of a political candidate saying something controversial or offensive could be used to sway public
opinion or damage their reputation.
Fraud: Deepfakes can be used for financial fraud or extortion. For example, a deepfake video of a CEO could be
created to instruct an employee to transfer funds to a fraudulent account.
Blackmail: Deepfakes can be used for blackmail. For example, a deepfake video of a person could be created without
their consent and used to extort money or to damage their reputation.
Cyberbullying: Deepfakes can be used for cyberbullying or harassment. For example, a deepfake video or image of a
person could be created to embarrass or humiliate them.
Disinformation: Deepfakes can be used to spread false information or propaganda. For example, a deepfake video
could be created to make it appear as though a public figure said or did something that they did not, which could
damage their reputation or influence public opinion.

Real Life Incident
Facebook founder Mark Zuckerberg was the victim of a deepfake that showed him
boasting about how Facebook "owns" its users. The video was designed to show how
people can use social media platforms such as Facebook to deceive the public.
U.S. President Joe Biden was the victim of numerous deepfakes in 2020 showing him in
exaggerated states of cognitive decline meant to influence the presidential election.
Presidents Barack Obama and Donald Trump have also been victims of deepfake
videos, some to spread disinformation and some as satire and entertainment.
During the Russian invasion of Ukraine in 2022, Ukrainian President Volodomyr
Zelenskyy was portrayed telling his troops to surrender to the Russians.
In 2019, a deepfake video of the Speaker of the US House of Representatives, Nancy
Pelosi, was circulated on social media. The video was manipulated to make it appear as
though Pelosi was slurring her words and drunk, which was used to attack her
credibility and reputation.

What Can We Do?
• As of now, deepfakes aren’t a huge problem, but they’ll likely
increase in prevalence and quality over the next few years.
That doesn’t mean you can’t trust any image or video, but you
should begin to train yourself to be more aware of fake images
and videos, especially when the videos are asking you to send
money or personal information, or making outrageous claims
that seem unusual for the person who appears to be making
them.
•
• Interestingly, AI may be the answer to detecting deep fakes.
Models can be trained to recognize fake images on dimensions
that the human eye can’t detect. Keep a watchful eye on the
development of the deepfake phenomenon over the next
couple of years, and, as always, remain vigilant

Detection of Deep Fake
• Unusual or awkward facial positioning.
• Unnatural facial or body movement.
• Unnatural coloring.
• Videos that look odd when zoomed in or magnified.
• Inconsistent audio.
• People that don't blink.
• Misspellings.
• Sentences that don't flow naturally.
• Suspicious source email addresses.
• Phrasing that doesn't match the supposed sender.
• Out-of-context messages that aren't relevant to any discussion,
event or issue.

Prevention of Deep Fake
Companies, organizations and government agencies are developing technology to identify
and block deepfakes. Some social media companies use blockchain technology to verify the
source of videos and images before allowing them onto their platforms.
Deepfake protection software is available from the following companies:
• Adobe has a system that lets creators attach a signature to videos and photos with
details about their creation.
• Microsoft has AI-powered deepfake detection software that analyze videos and photos
to provide a confidence score that shows whether the media has been manipulated.
• Operation Minerva uses catalogs of previously discovered deepfakes to tell if a new
video is simply a modification of an existing fake that has been discovered and given a
digital fingerprint.
• Sensity offers a detection platform that uses deep learning to spot indications of
deepfake media in the same way antimalware tools look for virus and malware
signatures. Users are alerted via email when they view a deepfake.

Are Deep Fakes legal?
Deepfakes are generally legal, and there is little law enforcement can
do about them, despite the serious threats they pose. Deepfakes are
only illegal if they violate existing laws such as defamation or hate
speech.
The lack of laws against deepfakes is because most people are
unaware of the new technology, its uses and dangers. Because of this,
victims don't get protection under the law in most cases of deepfakes.

Deep Fakes Statistics
How Many People Know What a Deepfake Is?
Globally, 71% of respondents say that they do not know what a deepfake is. Just under a third of global consumers
say they are aware of deepfakes

How Many People Think They Could Spot a Deepfake?
57% of global respondents say they think they could spot a deepfake. 43% admit they would not be able to tell the
difference between a real video and deepfake.

What Do People Think About Deepfakes?
• People are most concerned that deepfakes “could make it hard for us to trust what we see online”.
• The runner up was that “deepfakes are dangerous” – 62% of global respondents agree.
• 58% also agree that deepfakes are a “growing concern”.

Conclusion
• In conclusion, deepfakes have the potential to significantly impact society in both positive and
negative ways. While they can be used for creative and entertainment purposes, the malicious use of
deepfakes can result in the spread of misinformation, defamation, and other forms of harm. The
increasing accessibility and sophistication of deepfake technology has made it difficult to detect and
prevent deepfakes, but efforts are being made to develop effective solutions.
• To address the challenges posed by deepfakes, it is important for individuals, organizations, and
governments to work together to raise awareness about the risks and potential harms associated
with deepfakes, and to develop and implement effective detection and prevention strategies. By
doing so, we can help ensure that the benefits of deepfake technology are realized while minimizing
the risks and potential negative impacts.
Check out this Website
https://this-person-does-not-exist.com/en

Thank You!
N I L A Y D E S H M U K H

Deep Fakes Artificial Intelligence.pptx

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