Unmasking Deepfake: History, Implications & Detection

In this enlightening blog post, we delve into the intriguing world of deepfake technology. Discover what deepfake is—a sophisticated form of AI-based manipulation that convincingly alters or replaces visual and audio content. Explore the fascinating history of deepfake, from its origins to its rapid evolution. Uncover the dark side of deepfake, including its potential to deceive, manipulate, and harm individuals and society. Lastly, explore the cutting-edge techniques and tools developed to detect and combat this emerging threat to media integrity and trust.

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What Is Deepfake?

Deepfake is a technology that enables the creation of realistic and manipulated content using artificial intelligence and deep learning techniques. Deepfake is a compound word derived from the words "deep learning" and "fake". Deepfake technology is based on artificial neural networks trained with deep learning algorithms. These neural networks learn based on large amounts of data and then use that learning to create realistic images or videos. They are produced by combining and superimposing existing media on source media using machine learning techniques known as autoencoders and generative adversarial networks (GANs). In particular, it can successfully imitate details such as facial expressions, facial expressions, speech movements.


When Did Deepfake Appear?

The term deepfake is a concept used for artificial intelligence-based videos that spread on the internet in 2017. However, deepfake technology has a history dating back to the 2010s.

The origins of deepfake technology are linked to the development of deep learning and artificial intelligence algorithms. Deep learning has emerged as a field that focuses on artificial neural networks that can recognize patterns in large data sets and perform complex tasks. Deep learning algorithms have made great strides in areas such as face recognition, speech synthesis, and image processing.

Deepfake technology has attracted attention with the emergence of deepfake videos, which became popular in 2017 and went viral on the Internet. These videos used a technique that made it possible to create realistic and misleading videos by replacing people's faces with other people's faces. Deepfake technology has developed rapidly since then and has been used by more and more people.


Using Deepfake

Deepfake technology has become very popular in recent years and its use has become increasingly common. Currently, there are many used deepfake applications. While most of these are apps made for people's entertainment, they can also be used for various other purposes. As a result, many uses of deepfake have emerged. Although it was used for entertainment purposes at first, it started to be used in more areas as its qualities were discovered. For example, some artists are able to create works of art using deepfake technology creatively. To give another example, it can be used in the cinema and television industry for purposes such as bringing back the youth of the actors or transforming them into a different character. This can be beneficial to reduce costs and streamline the post-production process. It also allows for correcting an error-prone dialogue without the need for reshoots.

It was announced that Samsung's Artificial Intelligence Center in Moscow has developed a technology to obtain high-fidelity fake video from a single face photo or painting. While Mona Lisa's mimics were moved with deepfake technology, researchers had videotaped the photographs of famous names such as Marilyn Monroe, Fyodor Dostoyevski and Albert Einstein, as well as the Mona Lisa painting.


The Downsides of Deepfake

So is deepfake that innocent? Is it used only for useful and good works for humanity? Although deepfake technology was initially used for entertainment, it also brought with it potential risks of abuse. Deep learning technologies called deepfake examine photos or videos of real people and imitate the same behaviors with fake content, learn speech patterns, make lip movements and apply gestures and facial expressions. So it becomes quite easy to put a fake copy of one person on top of another. Persons incarnated by the fake face can be held responsible for things they never said or did because they took the place of the target persons. This leads to confusion, fraud and fraud. If this technology is used improperly, people can be punished even if they are innocent. In particular, fake video or photo content of famous actors, politicians, singers and politicians, known to the public, may cause them to be excluded from society, crushed under social pressure, and legal problems.

Consider, for example, ex-US President Barack Obama making insulting remarks or saying things about other presidents that he would never say in public. Undoubtedly, it would cause a great turmoil and crisis in the society. Then if you saw a video of such a speech, would you believe it? Or what would your reaction be if you came across a video of Morgan Freeman saying "I'm not Morgan Freeman"? Although it may seem a little scary, it is possible to make such videos with deepfake and it is done a lot.

Possible abuse of deepfake technologies has raised privacy, reputation and ethical issues. Therefore, research and technological advances continue to identify, detect and prevent deep fake content.

Fake or Real?

Images and videos produced with deepfake technology are spreading uncontrollably on social media and many internet channels. These images are getting closer to reality day by day and it is very difficult to understand whether they are real or not. However, a number of methods and studies are underway to combat deepfake technology and detect deepfake videos. Here are some:

  • Binary Classification Algorithms: Binary classification algorithms are being developed using machine learning and artificial intelligence techniques to detect deepfake videos. These algorithms try to detect deepfake videos by analyzing the differences and features between real videos and deepfake videos.
  • Image and Audio Analysis: Image and audio analysis techniques are used to find and analyze traces of deep learning methods used in the creation of deepfake videos. These methods can detect inauthentic features or certain patterns in deepfake videos.
  • Database and Monitoring: Large databases are being created to detect deepfake videos. These databases contain existing deepfake videos and actual videos used for deepfake generation. These databases allow comparison in detecting deepfake videos.
  • Collaboration and Research: Academic institutions, technology companies, and government agencies collaborate to detect and prevent deepfake technology. Research and development in artificial intelligence, security and media aims to improve the detection and prevention of deepfake videos.

Today, many large technology companies are also working on this issue. An example is Intel's deepfake detector. In addition to these studies, raising awareness about deepfake technology and educating people about deepfake videos is also an important step. Society needs to consume deepfake videos more consciously and be more careful about recognizing fake content.


Deepfake technology is developing rapidly as a field that pushes the boundaries of visual and auditory manipulation. In addition to its creative uses and entertainment potential, this technology raises important concerns such as the spread of fake news, privacy violations and security risks. Therefore, it is important to develop detection algorithms, make legal regulations and raise public awareness to combat deepfake technology. Understanding the potential of deepfake technology and acting responsibly towards it will play an important role in future developments. The days when we will question how much we should trust our eyes may be quite close.