In the era of fashionable digitalization, the world is more desirous to welcome new applied sciences to render folks modern benefits and make life a tad bit easier. Over a couple of years, a number of upgrading organizations endure a flow of digitalization and processed with combining applied sciences following cloud computing into digital transformation, i.e. the practice of Artificial Intelligence(AI) and Machine Learning(ML) has risen within the “Deepfake” world.
The means and proficiency required for producing wicked AI and ML codes are becoming more conventional, therefore a lot of data is available there for hackers to collect and utilize, which can be one of the causes of cybercrime.
Deepfake, on the other hand, is another technology that is gradually used to present practical AI- produced videos of real people making and speaking fictional things, and have critical implications to recognize the legitimacy of data displayed online, again another example of cybercrime.
In this blog, you will understand the concept of deepfake technology, its benefits, and example, technical implications and threats of deepfake technology.
“In simplistic terms, deepfakes are falsified videos made by means of deep learning” - Paul Barrett, adjunct professor of law at New York University.
The blend of “deep learning” and “fake”, deepfakes are basically realistic videos that are digitally manipulated to portray an individual saying and doing things that actually never happened. Deepfakes depend upon neural networks that analyze huge datasets to learn imitation of a person’s facial expressions, behaviourism, voice, and sounds.
The method includes injecting footages of two persons into deep learning and algorithms to train it to swap faces. Deepfake uses a facial mapping technique and AI to swap the face of a person into the face of another person on a video.
It is very hard to detect deepfakes as they use real footage, have authentic real voice-based audio, and have the capacity to spread over social media so quickly. So, many viewers find them genuine.
Deepfake marks social media platforms first because of rumours, conspiracies, and misinformation circulated very easily there and users favour to go with the crowd.
In terms of technology, deepfake are the products of Generative Adversarial Networks(GANs) in which two Artificial Neural Network works combinedly to design real-looking media. These two networks are called the “generator” and the “discriminator” to train the dataset of images, videos, and sounds.
Where the ‘generator’ attempts to make new samples that are good enough to deceive the ‘discriminator’, it determines the status of new media whether it looks real or fake. In this way, both networks assist each other to improve.
A GAN looks after thousands of images of a person and generates new portraits that are approximately the same without being an accurate reflection of prior images.
As deepfake are videos and images that are created with computers and machine learning algorithms and tools to make them appear real when in reality they are not. Specialists estimated that deepfake can be used to begin trouble and to generate false assumptions, especially in the context of a persons’ reputation, which is hard to detect. Consider the following examples;
Any political figure can easily be the target by creating fake footage of them speaking or doing things they never said or did, in order to change public opinion.
Film stars, external leaders, company profiles, presidential aspirants, religious bodies, and other famous authorities get bombarded with the pace of deepfake technology.
Fake emergency forecasts, fake and false information during the election, misleading while election campaigns, terrorist promotion, etc are the scenario that went worse with the commencement of deepfake technology.
Deepfake has proven beneficial in its application in a number of industries sheltering educational and social media, film industry, digital communications, games, entertainment, healthcare, applied sciences, and various business domains such as fashion and e-commerce. Some of the benefits are discussed below;
Deepfake technology has risen film industries more rapidly, it can help in composing digital sounds for actors who lost their voice due to disease or updating any footage of film instead of reshooting it. Filmmakers can recreate the traditional scenes in movies, produce new movies starring long-dead actors, create special effects and featured-face editing after post-production and improve video quality more professionally. (See how IoT in movies help in this frame)
Deepfake technology enables users to play multiplayer games and virtual chat worlds such as natural-sounding, smart assistance, digital twins of people, similarly presented in virtual reality. It gives better human relationships and interaction online.
The potential applications of deepfake technology surely are enhancing businesses digitally. It could possibly transform e-commerce and advertisement in significant ways. The famous brand can tie up with supermodels who are not actually supermodels and can show their fashion outfits on the class of models with different skin colours, heights, and weights. It helps in generating targeted fashion ads that vary according to time, weather and viewers, especially in the online clothing business.
While discussing the consequences of deepfake technology, we should not forget the fact that AI has both positive and negative sides, developers are concerned when and how to improve and apply technologies that truly benefit people and the world, and especially how to make the involvement of society in their development.
For instance, where the development of deep generative models breeds new possibilities in healthcare, we are also concerned about the patients’ privacy in their treatment and continuous research which needs to be resolved.
(Must read: AI in healthcare)
There is nothing new with the manipulation of videos, people use this trick to make the audience believe something is real such as in films. But, deepfake has prefaced a new level of authenticity into action.
Deepfake image example
“This is developing more rapidly than I thought. Soon, it’s going to get to the point where there is no way that we can actually detect [deepfakes] anymore, so we have to look at other types of solutions.” - Hao Li, Deepfake Pioneer
The approach of learning computer programs to rush things by itself and developing its information base to grow more cosmopolitan is scary. From the past few years, cyber-threat has become a serious matter of concern.
For instance, destroying or putting down websites and stealing credit card data are major instances of cyberattacks, these attacks are costly as they require attackers to dedicate time and efforts. However, presently various types of cyber threat can be cured with the implementation of IoT.
With the help of AI, an attacker can drag out huge information and repeat multiple attacks through a few lines of code. Some of the threats of deepfake technology are mentioned below;
AI software deployed to produce deepfake would possibly be a distinct device outside to the camera. Today’s cameras are more powerful as to be mini computers and hence not be an effective platform to perform deepfake activities.
So to execute deepfake activities, other separate devices are being used to manipulate original videos produced by the camera.
Another real threat from deepfakes as recognizes and access management unites with any physical security is biometric algorithms in facial recognition.
New IP-based infrastructures are designed by enterprises that incorporate physical securities, IT and operational technology internally connected, so the use of smartphones for biometrics and multifactor authentication is implemented. Now smartphones can give access to protected premises.
Facial recognition-based biometric authentication
The making of memes can endorse someone to believe a set of facts, real or not. Many people hold biased confirmation that changed to be real beliefs when they look over any facts, stats, or hot takes. This biased confirmation increases when a video or images brought into the mix. So, it falls on us to interpret what is fake and what is real.
(Also read: Detection of fake news with CNN)
Including fabulous examples and applications of Machine Learning, Deepfake is a spectacular technology that provides practical, easily understandable, and real-life applications. Yet most of the latest uses of it are deceitful.
With the elevated technology, deepfake are the fruits of deep generative modelling, a recent technology that enables to produce replicas of original faces and builds new and elegant vivid portraits of people who never exist. Moreover, deepfake has put up a set of claiming policies, technology, and legal concerns.
Being a user we should check the originality of everything we observe, overhear or browse online. In the deepfake world, being reliable users of technology make sure to define the legitimacy of every tad of information spread via media, rather foolishly accepting everything. Surely, during this read-vein, you acquired the notion of deepfake and its relevant aspects.
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
6 Major Branches of Artificial Intelligence (AI)READ MORE
Top 10 Big Data TechnologiesREAD MORE
Introduction to Time Series Analysis: Time-Series Forecasting Machine learning Methods & ModelsREAD MORE
What is the OpenAI GPT-3?READ MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What is TikTok and How is AI Making it Tick?READ MORE