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What is Deep Fake & it’s impact on security in 2025?

by VPN Guider

March 3, 2025

What is Deep Fake?

Deepfake is a form of synthetic media where a person’s existing image or video is altered to replace their likeness with someone else’s. This technology relies on artificial intelligence (AI) and machine learning to create fabricated events, often used to manipulate individuals for personal or malicious purposes.

As a specialized application of AI, deepfake technology uses deep learning techniques to generate or alter audio and video content with a high potential to deceive. While the concept of faking content is not new, deepfake technology has advanced significantly, enabling the creation of highly realistic fake media. Examples include celebrity pornographic videos, fabricated speeches by politicians, and even financial scams.

The core machine learning methods behind deepfakes are rooted in deep learning. These methods involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs), to produce convincing fake content. As of 2025, deepfake technology continues to evolve, raising concerns about its ethical implications and the need for robust detection methods to combat its misuse.

Deep Fake

Interesting Usage of Deep Fake Technology:

Dubbing, improving and repairing video content
Solving effects in Video games
Avoiding actors having to repeat fluffed lines or mistakes.
Creating apps that allow people to try clothes or glasses.

Negative aspects of Deep Fake:

Misusing celebrity images/videos for explicit adult content
Financial Crimes
Misusing politician’s images/videos to create false propaganda

How are Deepfakes Made?

Deepfakes Made

At first, a user runs hundreds of face shots of the two people he wishes to swap through an AI algorithm called an encoder. The encoder finds and learns mutual features between two faces and lowers them to their shared standard features, thus, compressing the images.

A second AI algorithm, called the decoder, is then taught to recuperate the faces from the compressed images. As the faces differ, one decoder is trained to recover the first person’s face while another recovers the second person’s face. To perform the face swap, you feed encoded images into the “wrong” decoder.

FAQs

A deep fake is a neural network that is adaptive at various functions, including generating, interpreting high bandwidth media, and generating fake press. In deepfake, you can take voice of one person and apply a different style to it, sounding like someone else. It could be anything from a modified and edited audio, video or image file.

Identifying a deepfake is sophisticated for a layman. Much research is going on to counter the technology and avoid its harmful effects. The best way is to examine the original content of that person and then try to look at jaw movement, eye blinking and facial motion.

In 2012, deep faking suffered on the dark corner of the internet. Today, it is famous for gimmicky memes. However, it is a severe threat to the privacy and security of people.

All tech and social media giants, including Facebook, Twitter, and LinkedIn, focus on avoiding deepfakes. The research is being done to technologically address deepfakes like embedding digital watermarks and fingerprints and using signatures inside file metadata.

How to Detect them?

Deepfake initially came into existence when a reddit user named “Deepfakes” posted that he developed a machine learning (ML) algorithm that could convert celebrity faces seamlessly onto porn videos. The thread soon became very popular, spawning its subreddit account.

Hence, to detect deepfakes, there is also technology built through AI using algorithms. They detect signs that wouldn’t be on real photos/videos. Initially, it was easy to catch deepfakes due to poor synchronisation, patchy skin tones or weirdly rendered jewellery. However, due to the latest technological advancements, detecting deepfakes is very difficult. Governments, universities, and tech firms all fund research to detect deepfakes.

Impact/Threats on Cybersecurity

Deepfakes has raised eyebrows and opened up conversations about the seriousness or even dangers of its technology. It can target warfare, organisations, politicians or almost everyone to increase or kill morale.

In March 2022, Sensity, an intelligence company specialising in visual threats, recorded 85000 deepfake videos. These were meant to destroy reputations, consisting of non-consensual pornography, inciting fear and violence through fake speeches from targeted politicians.

Additionally, in 2019, the wall street journal reported. The CEO of a UK-based company instantly transferred 220,000 euros, believing the orders were from his boss.

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Conclusion

Deepfake severely impacts security and is considered one of the most dangerous uses of Artificial Intelligence. They can promote the read of misinformation and trigger new crime schemes and information wars as they tend to erase the divide between true and false content.

With the development of technology and related applications, DF creation becomes available to many people, including perpetrators, abusers, and criminals. The critical problem is that given the nature of GANs and other digital content forgery technologies, DFs are hardly detected. We have yet to fully recognise and realise all threats that the DF can pose in the future.