Deepfake: What is It and How to Stay Safe?

Separating fact from fiction when surfing the web has become increasingly complex over the past decade. People are less likely to examine the information they consume as their dependence on electronic communication, and virtual collaboration technology has increased.

Deepfake cost news exceeding $78 billion each year, detecting the warning signs has become critical for company profitability.

In addition, the advancement of technology, such as deepfake, has boosted the production of manufactured information, increasing its cost.

This article will describe and analyze deepfakes, discuss their influence on cyber security, and teach you how to avoid becoming a deepfake target.

What exactly is a Deepfake? The History of this Groundbreaking Technology

Deepfakes are fake photos, films, and audio recordings created using computers and software. Synthetic material that seems natural is created by combining video and pictures. The name comes from a combination of the words deep learning and forgery.

It’s difficult to say when this technology was developed as it dates back to the 1990s digital video and imaging technologies. However, the first successful deepfake effort was made and shared on Reddit in 2017. The user forged obscene films by superimposing celebrities’ faces on the performers.

How Does a Deepfake Operate? Bringing its Hidden Face to Light.

Creating a convincing deepfake requires the use of artificial intelligence (AI). Deep learning and a Generative Adversarial Network (GAN) are the two most widely used methods.

The first technique loads thousands of images of two people into an encoder. The AI program then evaluates all input information for similarities and compresses the photos. The faces are then extracted from the compressed photos using decoders for each individual. Finally, person A’s features are superimposed over person B’s face, resulting in plausible synthetic content.

Two machine learning (ML) algorithms compete in the GAN approach. The first algorithm, the generator, generates synthetic images from data. The second algorithm, known as the discriminator, blends the generator’s synthetic pictures with the real ones. Repeating the technique several times allows the ML to generate lifelike faces of random people. This method takes the most time and is the most effective way to make deepfake pictures.

Deepfake audio is created by running the original material through an AI algorithm that clones the person’s voice, tone, and mannerisms. Deepfake audio can be created from uninterrupted speeches, presentations, interviews, phone conversations, and corporate films.

How Simple Is It to Make a Deepfake?

Access to software that allows for the construction of deepfake has expanded dramatically in recent years. As a result, even those with no photographic or technological skills can now create such materials.

Individuals can use mobile phone applications like Zao to put their faces on actors in pre-selected movie and TV program sequences. The method only requires one photo of the user. The app’s technology then replaces the person’s face with that of the actor, producing believable results. This problem is exacerbated by software and desktop programs such as FakeApp.

Despite their complexity, the applications provide instructions, making them simple for anybody with intermediate technical abilities to use.

It’s also worth noting that a new Chinese AI algorithm can successfully clone a voice using a 4-second audio clip, allowing for enough key voice characteristics to be deciphered and recorded. A year ago, doing this identical task needed 30 minutes of audio information and several audio segments.

However, only specialists can create high-quality deepfake. Imperfections and aesthetic flaws must be removed using high-end computers. Depending on the format and program, this procedure could take several weeks.

What are the Consequences of a Deepfake for Cyber Security?

Although many people praise deepfakes for their usefulness, such as for entertainment, the technology poses a threat to cybersecurity. By making identity theft more credible and aiding vishing and spoofing attacks with video and audio alterations.

Deepfake cyberattacks can be directed at anyone. With easy access to deepfake tools, software, and even websites, anybody may become a victim.

Because of social media, your identity might potentially be utilized in a deepfake assault. Photos and videos of yourself that have been posted on the internet can be recovered and stored in the program.

Deepfake is used to manipulate audio content in a corporate context and allows scammers to impersonate workers, salespeople, or executives of a specific organization during phone conversations. The fast-growing presence of businesses on social media significantly impacts their exposure to such a threat.

In early 2020, a well-known deepfake scam incident made headlines. A bank manager in Hong Kong allowed the transfer of $35 million after speaking on the phone with who he believed to be the company’s director. The bank manager had previously spoken with the director, but this did not save the manager from being duped.

Methods for Detecting and Combating Deepfake

Four basic strategies can be implemented to defend against deepfake.

1. Perform a visual examination of what you’re looking at. This includes examining how the individual in the video blinks and the visual quality.
2. Maintain vigilance. It is vital to cultivate a critical mentality and confirm the source of the material.
3. Use artificial intelligence to detect anomalies that the human eye may miss.
4. To ensure the speaker’s identity, begin a phone call with a secret password or question.

Recap

Deepfake is becoming increasingly popular, and there is an absence of laws on the issue, both of which pose cybersecurity risks. In addition, advances in artificial intelligence have aided the creation of convincing deepfake. As a result, it’s important to be alert and cautious and exercise cyber security. This includes being mindful about what you post online and not trusting everything you read.