Meta Description: Deepfake technology can create fake images and video content by analyzing the voice modulation, speech patterns, and movement of a specific person encoded into the system
In passing decades, there has been an increasing wave of technological revolutions and innovative concepts bringing more convenience and value in the life of the masses. Many businesses were able to meet enhanced efficiency levels and provide commendable user experience to their potential customers. But the massive potential of AI-driven technologies has also been invested in illegitimate and immoral activities.
Potential criminals are getting swift and equipped in their game by implementing various technologies to create fronts, synthetic IDs, and access sensitive data of people. In past years, frauds in the financial services industry and corporations have gone to the next level and one technology being most common in every scenario is Deepfake.
What is Deepfake and Where Did it Start from?
Deepfake technology was introduced to people earlier in the 90’s era, by the television and entertainment industry, as it is clear from the name, the OCR technology creates perfect but fake video content of individuals that is hard to identify. Deepfake technology was initiated to create gigs and laughs. Humorous content used to be put out there, where the AI-based technology would artificially imitate a famous soul, make him/her say funny things, or perform comedy sketches, which started out as a sincere attempt to entertain the audience later became a threat for the very same people.
Fraudsters and hackers by implementing Deepfake can create fake content for any person for their personal gain, making him quote whatever they want. The global ID verification providers are striving to come up with advanced KYC/AML verification solutions for banks and corporations to ensure legitimate customer onboarding and prevent such scammers.
How does Deepfake Technology Create False or Misleading Content?
At first, the creator takes multiple face shots of both persons he/she wants to replace and his model from different angles. The facial depth of both people and other data is processed via a set of automated neural network performances called Encoders. The algorithm of Deepfake technology compares and recognizes facial similarities between both of them and eliminates the rest of the data.
Now with a shared pair of similarities, the Decoder comes under implementation. The system uses two different decoders for the recovery of faces of both individuals. Afterward, for end results, the encoded data of both persons are incorporated into each other’s decoder.
The encoded images of the original personality will be transferred to the decoder working on the face of a synthetic one and vice versa for successful face replacement.
Reported Cases of Deefake Technology’s Misuse
After many years of its arrival, the first scam was reported in 2019 when a CEO of a firm received a fake call, created via Deepfake technology from what he thought was his boss and it demanded an immediate transfer of almost $243,000.
Similarly, an employee of a tech firm also got an AI-based call demanding a huge amount on an urgent basis in 2020, which wasn’t successful and there are other few cases but only because any major financial attack has not been carried out, people are not taking it seriously.
Can Anyone Misuse Deepfake Technology?
With technological access and computer programming expertise, loads of people can pose a greater financial risk.
Back in 2018, a video statement by Barack Obama, then USA president, got released in the press, where people could clearly see him delivering a speech but it was not him. An American actor Jordan Peele created a deep fake video of the president just to make everyone realize how perfect and hard the deep fake video is to distinguish from the original one and can deceive anyone.
Other than that, much false media content is created through deepfake technology by targeting TV personalities, actors, politicians, and others. Inappropriate images and videos of every possible female celebrity have been created and scandalized.
Deepfake content is easier to generate than people think. The machine learning algorithms of the technology creates desired content by analyzing the voice modulation, speech patterns, and movement of a specific person encoded into the system.
There are so many trending apps in masses like FaceApp that although create false videos that are clear to identity document verification but still the level of work done by a normal mobile application showcases that it is not that tough for advanced AI-driven technology.
How to Spot a Deepfake Video?
To detect deepfake content, there are several AI-powered counter technologies that function on a similar set of algorithms, recognizing false content elements. To name a few;
- The most common one is speech and lips not being synchronized which is comparatively the most important one to make look original
- The resolution of the face does not match with other parts of the body
- Changes in skin tone
- Unusual or abrupt movements
- The body language of the person acting against words spoken
Conclusion
Ever since its introduction, deepfake technology was not healthily used for fraudulent operations but the advancements brought by FinTech and other industries have made cybercriminals and hackers look for sophisticated technologies and eventually the sheer range of this technology and how it can create panic and commit financial frauds is realized. It has targeted several famous celebrities and politicians and is considered to be a major threat for industries with technological revolutions. Therefore, advanced AI-driven solutions should be implemented for IDV and laws for restricting deepfake use.
