Today, we live in a technologically controlled world where organizations employ digital services for everyday operations. Now, companies onboard users and partners remotely through an online process. They utilize digital processes and allow remote access to the platforms. Digital processes made it easy to have streamlined working operations. However, they also made organizations vulnerable to criminal attacks. With the help of machine learning technology, fraudsters generate spoofed data to utilize for bypassing securities. Neural networks allow them to generate deepfakes that are difficult to identify through the human eye. Hence, there is a need for the most appropriate solution, such as deepfake detection technology, to secure organizations from cybercrimes.
What are Deepfakes? – An Overview
Deepfakes are complex media that are generated with the help of neural networks and machine learning technology. It is difficult to identify deepfakes with simple security methods. Cybercriminals utilize complex and advanced strategies such as deepfakes to perform illicit activities. Many scammers contact higher-level firms with deep fake audio and videos that cannot be identified without advanced security protocols. Deepfake detection technology is another side of artificial intelligence that is used as a preservative and security from the same technical problems such as spoofed data and fake videos.
Nowadays, companies establish remote partnerships and onboard customers digitally. They often fall prey to cyber criminals for using weak security. Fraudsters have learned to dodge security methods. They reach out to organizations for data breaches, financial terrorism, and money laundering. Hence, there is a need for appropriate technology to prevent fraud attacks within the organizations.
How Do Criminals Generate Deepfakes?
Deepfake creation is simple, but it is the product of complex techniques that involve artificial intelligence and neural networks. Initially, a large amount of data is collected from individuals, which has to be replicated. This data involves images, videos, and the voices of a specific person. Then, algorithms are trained and commanded to replicate the identity of an individual. A synthetic media is generated with the help of artificial intelligence. This media can be in the form of audio, videos, and images.
Criminals generate a deepfake of well-known business owners and use them to approach various organizations in the name of investments and partnerships. Companies that do not utilize up-to-date security measures are vulnerable to cyber crimes.
Why is Deepfake Detection technology Necessary to Utilize?
Deepfake detection technology is necessary to combat prevailing fraud attacks. It is necessary to have up-to-date security methods to identify various kinds of synthetic data. Without deepfake detection technology, companies cannot identify fake audio and videos that are generated through artificial intelligence. There are various examples of cyber-attacks which are the result of deepfake generation.
In 2018, US President Barack Obama underwent deepfake attacks. His video came in from which he was delivering a message that he never said. Later, with the help of detection technology, it was determined that it was a fake video. It was a deepfake video that was created by actor Jordan Peele in collaboration with BuzzFeed CEO of BuzzFeed.
The above example showed that even political celebrities are not secure from deepfake attacks. Once, a company got a voice call from its CEO to share a specific amount to a specific bank account. Later on, they identified that his CEO had not called, but it was a deepfake. This is how scammers utilize deep fake technology for cyber attacks.
Many criminals utilize deepfake technology to generate videos of big investor companies’ CEOs. They use these videos for video interviews and discussions to reach networks for heavy financial losses.
What are Deepfake Detection Solutions?
There are various deepfake detection solutions that companies use to prevent cyber attacks. However, there is a complete service designed for deepfake prevention, and it is deepfake detection technology. It involves the usage of biometric face verification technology and various other interrelated steps to validate data before onboarding specific entities. The face verification process within the deepfake detection technology involves machine learning technology to verify facial data. It verifies the complex facial nodes, including muscle stretching, skin patterns, and various other features, to identify the nature of the media presented.
Companies can utilize automated deepfake detection technology to verify entries in real time for a secure business landscape. It helps to eliminate cyber attacks and prevent fraud in organizations.
Final Words
Deep Fake detection technology is necessary to utilize for a secure business landscape. It works through neural networks and verifies user IDs for real-time security from cyber attacks. This technology involves automated checks and various steps to identify the nature of data. Hence, organizations utilize biometric face verification services as deepfake prevention solution. However, a complete process of deepfake detection technology involves facial verification along with other steps to identify the nature of data.
About the Author
Ryan Jason is a Technical Content Writer who’s been writing about Cyber Security, Blockchain and Cryptocurrencies for over 3 years. His mission is to draw people closer to cyberspace by providing them with actionable and helpful content. He has a wide variety of writing experience in the Artificial intelligence, Blockchain, BigData, FinTech, Crypto, DeFi, Ethereum, and Cybersecurity sector.
Ryan can be reached online at [email protected] and https://www.linkedin.com/in/ryan-jason-a87b381b7
