In the remote onboarding process, it has become necessary to identify if the provided data for the verification process is authentic or not. With a rise in technological advancements, organizations are becoming more vulnerable to fraudsters and criminal attacks. Nowadays, fraudsters utilize deepfake technology as an important part of their strategies.
Deepfake technology, as being in the hands of scammers and fraudsters, is highly dangerous. In the past few years, there have been deadly consequences of deepfake technology in the form of misinformation spread, reputational damage, and financial loss. As per the report, in the year 2023, 62% of female and 60% of male adults have responded to the survey with a serious concern about the overgrowing effects of deepfake technology.
This blog post will add value to understanding the nature of deepfake technology, and it will further help identify solutions to prevent the deadly consequences of deepfake technology.
Deepfake Technology – A Brief Overview
Deepfake technology is one of the types of artificial intelligence that allows its usage to create fake data in the forms of images, videos, and audio. The word deepfake in itself exhibits its consistency, ‘deep learning’ and ‘fake,’ meaning the usage of deep learning techniques for the creation of fake content.
With the help of deepfake technology, it is easy to generate new content, which can be completely new or it can result from existing content. If someone has to generate a deepfake from existing content, here the swapping method is used. To create entirely new content, machine learning algorithms are trained on the basis of data of one individual, and then a replica is created.
How Does Deepfake Creation Take Place?
There are two main category algorithms working for the creation of deepfake. They can be divided into generators and discriminators. A generator is responsible for the creation of data as per the requirements provided regarding the desired outcome.
It generates a training dataset, which is provided to train algorithms for the creation of specific types of content. Large data sets are required to generate deep fakes , it includes real data of any entity which is used in training to produce desired video, image, or audio later on.
Discriminator category algorithms analyze the nature of content generators and identify the level of reality in generated content. It keeps on analyzing content and enables generators to create such realistic content, which is highly difficult to spot without sharp detectors.
Both generator and discrimination algorithms lead to the creation of generative adversarial networks (GAN). It uses deep learning technology to identify various patterns of actual data and utilize it as a database to generate fake content, such as deepfake videos of any well-reputed entity with such words he/she never said in reality. Algorithms verify an image or video to identify its visuals, speech patterns and behavior in it to later use in deepfake content.
Consequences of Deepfake Technology?
Deepfake technology has various deadly consequences in the form of reputational and financial loss. Fraudsters often utilize deepfake technology in phishing attacks and data breaches. They utilize deepfake audio of any significant member of the corporation and utilize it to obtain heavy transactions.
For example, once a company finance department got a phone call from their CEO to transfer a significant amount to a new account. Once they transferred the amount, they came to know that it was not their CEO but a scammer.
In many cases, deepfake videos are used to spread misinformation. It seriously brings harmful consequences in the form of reputational damage and mistrust. Once, former president Barack Obama was trapped to spread such a message, which he never said.
Fraudsters generate deepfake videos and images to utilize for bypassing securities, specifically during the remote onboarding process. They reach out to organizations for performing various illicit activities such as money laundering, financial terrorism, and data breaches.
Solution to Handle Deep Fake Attacks
To handle deep fakes attacks, it is highly important for organizations to utilize effective security protocols. Many companies offer deepfake detection technology, which involves machine learning technology and helps identify spoofed and fake content. This technology works through pre-trained algorithms and identifies AI-generated deep fakes that are difficult to spot with the human eye.
Additionally, the government should run campaigns to spread awareness regarding deepfake technology so that people can be secure from the deadly consequences of misinformation. There should be strict punishments for those who engage in a deep fake and use it for illegal activities.
Final Words
Deepfake technology has various deadly consequences at individual and group levels. It involves neural networks and generates images and videos that are difficult to identify with the human eye.
Deepfake detection technology is the most appropriate solution that organizations can use to secure their landscape from fraudster attacks. The government should run awareness campaigns to make people aware of deepfake technology and overcome misinformation.
