Deep Learning

Deepfake Technology – Examples and Threats

Modern digitalization has made the world more open to new technologies that can give people modern benefits and ease their lives a little. The practise of artificial intelligence (AI) and machine learning (ML) has become more prevalent in the “Deepfake” world as a result of a number of upgrading organisations going through a flow of digitalization over the past few years.

There is a lot of data available there for hackers to capture and use, which can be one of the causes of cybercrime, as the tools and expertise needed to produce wicked AI and ML scripts are becoming more conventional.

On the other hand, Deepfake is a different technology that is rapidly being applied to portray realistic AI-produced movies of actual people speaking and acting in fictional situations. It has important consequences for determining the veracity of data that is posted online, which is yet another type of cybercrime.

Deepfakes—a mashup of “deep learning” and “fake”—are essentially realistic recordings that have been digitally altered to show people doing and saying things that have never actually occurred. Deepfakes use neural networks to learn how to mimic a person’s facial expressions, behaviour, speech, and noises by analysing large datasets.

The technique entails feeding videos of two people into deep learning algorithms to teach it to switch faces. Deepfake transforms a person’s face into the face of another person in a video using a facial mapping technology and AI.

Deepfakes use actual footage, and authentic real voice-based sounds, and have the ability to quickly propagate across social media, making them incredibly difficult to spot. many viewers believe they are real.

Due to the ease with which rumours, conspiracies, and false information are spread on social media platforms and the tendency of users to follow the herd, Deepfake targets these platforms first.

Deepfakes are technological creations of Generative Adversarial Networks (GANs), which combine the efforts of two Artificial Neural Networks to create realistic-appearing media. The “generator” and the “discriminator” networks are used to train the dataset of pictures, videos, and audio.

The status of new media is determined by whether it appears to be real or phoney in cases where the “generator” makes new samples that are convincing enough to fool the “discriminator”. Both networks help each other to advance in this way.

A GAN monitors thousands of portraits of a person and creates new ones that are somewhat similar to old ones but not exact replicas of them.

Videos and images that are produced using computers, machine learning algorithms, and other tools to make them seem real when they are not are known as deepfakes. According to experts, deepfake can be used to start trouble and to make erroneous conclusions, particularly when it comes to a person’s reputation, which is difficult to identify. Think about the following instances:

  1. It is simple to attack any political figure by fabricating video of them saying or doing things they never said or did in an effort to sway public opinion.
  2. The pace of deepfake technology bombards movie stars, world leaders, corporate profiles, presidential contenders, religious bodies, and other well-known authority.
  3. The situation got worse with the introduction of deepfake technology, including fake emergency forecasts, fake and deceptive information during the election, misleading during election campaigns, terrorist promotion, etc.

The Benefits of Deepfake Technology

  1. Deepfake technology has accelerated the growth of the film industry. It can be used to update existing film footage rather than reshoot it, or to create digital sounds for performers whose illnesses caused them to lose their voices. In addition to producing new films starring performers who have passed away, filmmakers can also reproduce classic movie scenes, create special effects and featured-face editing in post-production, and enhance video quality more expertly. (In this frame, you can see how IoT aids the story.)
  2. Digital twins of humans who are similarly represented in virtual reality and that have natural-sounding, intelligent assistance are made available to users in virtual chat worlds and multiplayer games thanks to deepfake technology. Online contact and human relationships are improved.
  3. Deepfake technology’s future uses are undoubtedly improving digital business. It might have a huge impact on advertising and e-commerce. The well-known firm has partnerships with supermodels that aren’t actually supermodels and may showcase their clothing lines on a variety of models with various heights, weights, and skin tones. Particularly in the online apparel industry, it aids in the creation of customised fashion advertisements that change based on the time, weather, and visitors.

Consequences of Deepfake Technology

We shouldn’t ignore the fact that AI has both positive and negative aspects when discussing the effects of deepfake technology. Developers are concerned about when and how to advance and use technologies that genuinely benefit people and the world, particularly how to involve society in their development.

For instance, even though the development of deep generative models opens up new possibilities in healthcare, there are issues that need to be overcome regarding patient privacy during treatment and ongoing research.

The more widely used deepfake technology grows, the less confidence we have in our own eyes.

The trick of manipulating videos to deceive the viewer into believing something is real, like in a movie, is nothing new. However, deepfake has introduced a higher standard of authenticity.

Deepfake Technology as a Threat

It’s unsettling to think that learning computer programmes will allow society to advance more quickly and more globally. The possibility of a cyberattack has grown in importance during the last few years.

Examples of large cyberattacks include taking down or deleting websites and obtaining credit card information. These assaults are expensive since they demand the attackers’ time and effort. However, the use of IoT can currently be used to treat a number of different cyber threats.

With just a few lines of code, an attacker may repeat numerous attacks and extract vast amounts of information with the aid of AI. Below are some of the dangers posed by deepfake technology.

1. In video surveillance and its objectives:

A separate device outside of the camera may be used to make deepfake using AI software. Since today’s cameras are more powerful than small computers, they are ineffective as a platform for deep-fake operations.

Therefore, original videos created by the camera are being altered in order to carry out deepfake activities.

2. Recognising faces through monitoring

Biometric algorithms in facial recognition pose a genuine threat from deep fakes as they combine access control with any physical security.

Enterprises that integrate internal connectivity between operational technology, IT, and physical security create new IP-based infrastructures that support biometrics and multifactor authentication. Smartphones can now grant access to secure locations.

3. Controlling Fake News

Memes can encourage people to believe a collection of facts, whether they are true or false. When they review any facts, numbers, or hot takes, many people hold biassed confirmation that evolved into true views. When videos or images are included, this biassed confirmation rises. Therefore, it is up to us to determine what is true and what is fake.

Deepfake is an amazing technology that offers useful, simple-to-understand, and real-life applications, including wonderful Machine Learning examples and applications. However, the majority of recent applications of it are dishonest.

Deepfake is the result of deep generative modelling, a relatively new technology that makes it possible to create exact duplicates of real faces as well as new, tasteful, colourful images of people who have never existed. Deepfake has also posted a list of technology, legal, and claiming policy concerns.

As users, it is our responsibility to verify the authenticity of anything we see, hear, or peruse online. Being responsible technology users in the deepfake world means determining the veracity of every nuance of information disseminated through media, as opposed to carelessly accepting everything. During this read-vein, you undoubtedly learned about deepfake and its related concepts.

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