What is a deepfake?

What is a deepfake and how it can affect your business

Deepfakes are becoming a growing threat in both society and the business world. Deepfakes are a form of AI generated videos that leverage advanced artificial intelligence to create convincing forgeries. As technology advances, so does the ability to create hyper-realistic fake videos and audio clips that can deceive even the most alert viewer. The dual nature of deepfakes means they can be used for both innovative applications and malicious deception. But what is a deepfake exactly, and why should businesses be concerned in the digital age?

What is deepfake technology?

A deepfake is a manipulated video or audio recording that uses artificial intelligence to make it appear as though someone said or did something they never actually did. The word “deepfake” is a combination of “deep learning” and “fake,” reflecting the use of AI algorithms to create convincing forgeries.

Most deepfakes involve replacing a person’s face or voice with someone else’s, often taken from public content like interviews, videos, or podcasts. Facial recognition technology is used to identify and map a person's face and voice for manipulation. These synthetic media files are becoming increasingly difficult to distinguish from real ones, and they are being used not only in entertainment but also in fraud, blackmail, misinformation campaigns, and cyberattacks. Creating deepfakes often starts with an original video, which serves as the basis for generating manipulated content.

Deepfakes are made using advanced algorithms such as generative adversarial networks (GANs), which are the core technology behind these manipulations. Generative adversarial networks (GANs) consist of two AI systems working together to create increasingly realistic fake content. Creating deepfakes involves techniques like face swapping and voice synthesis, and presents significant challenges for detection. Deep art effects are sometimes used to enhance the realism of AI-generated visual content. Significant computing power is required to process large datasets and run the complex AI models used to create deepfakes. AI is used to create deepfakes for both entertainment and malicious purposes.

How deepfakes work

Deepfake technology harnesses the power of artificial intelligence and deep learning to create highly convincing fake images, videos, and audio recordings. At the heart of this process is a type of AI model known as a generative adversarial network (GAN). A GAN consists of two neural networks: the generator, which creates synthetic media, and the discriminator, which evaluates how realistic the generated content appears compared to real images, videos, or audio recordings. These networks are trained on vast datasets containing countless examples of a target person’s face, voice, or body, allowing the AI to learn intricate details and patterns.

As the generator produces fake content, the discriminator provides feedback, pushing the generator to improve its output with each iteration. Over time, this back-and-forth results in deepfake videos and audio deepfakes that are increasingly difficult to distinguish from authentic digital content. This technology can be used to swap faces in videos, mimic a person’s voice, or even create entirely new fake images that never existed. While deepfake creation can be used for entertainment and creative projects, it also opens the door to malicious uses, such as spreading fake content or impersonating individuals for fraudulent purposes. The sophistication of deepfake technology continues to grow, making it essential for businesses and individuals to understand how these synthetic media are made.

Examples of deepfakes

The rise of deepfake technology has led to a number of high-profile incidents that highlight both its capabilities and its risks. For example, deepfake videos featuring celebrities and politicians have circulated widely on social media platforms, showing public figures saying or doing things they never actually did. These manipulated videos can quickly go viral, influencing public opinion and spreading false information on a massive scale. In the business world, audio deepfakes have been used to impersonate executives, leading to cases of financial fraud where employees were tricked into transferring large sums of money based on fake audio instructions.

One notable example involved a deepfake video of a political leader making inflammatory statements, which was shared across multiple social media channels before being debunked. Another case saw a CEO’s voice convincingly replicated in an audio deepfake, resulting in a successful scam that cost the targeted company hundreds of thousands of dollars. These examples of deepfakes underscore the importance of critical thinking and the need for robust deepfake detection software to help identify manipulated content before it can cause harm. As deepfake content becomes more sophisticated, the challenge of detecting and responding to these threats grows, making vigilance and awareness more important than ever.

The business risks of malicious deepfakes

Understanding what a deepfake is helps businesses recognize the potential damage it can cause. Deepfakes are dangerous because they can be misused for misinformation, privacy violations, and causing reputational harm to individuals or organizations. While deepfakes are often associated with politics or social media hoaxes, they also present serious risks for companies. Deepfakes contribute to emerging threats in the business and cybersecurity landscape, requiring organizations to stay vigilant against evolving malicious technologies. Additionally, deepfakes can undermine democratic processes by spreading fake news and manipulating public opinion, which can erode trust in institutions and sway elections.

Financial fraud

One of the most alarming examples happened in 2019, when a UK-based energy company was targeted with a deepfake audio scam. An employee received a phone call from what sounded like the CEO of their German parent company. The voice urgently requested a money transfer of $243,000 to a supplier. Trusting the voice, the employee followed through. However, the caller was not the real CEO but a fraudster using AI-generated audio to impersonate him.

This type of social engineering is not new, but deepfakes make it much more convincing. Fraudsters can use voice samples from earnings calls, interviews, or social media to mimic executives and trick employees. If you want to learn more about how these types of attacks work, read our guide to social engineering.

Reputation damage

Deepfakes can be used to create false statements by company leaders, damaging trust with customers, investors, and partners. A fake video of a CEO admitting to fraud or misconduct could go viral before the truth comes out, leaving permanent reputational damage. You can delve deeper into our guide on CEO fraud if you'd like to know more about how it operates.

Stock market manipulation

If a deepfake shows a tech executive making damaging claims or announcing false information, it can influence investor behavior and cause stock prices to drop. In a world where markets react to headlines within seconds, a realistic fake video can be highly disruptive.

Cyberbullying and blackmail

Employees and executives can also be targeted personally. Malicious actors may create fake videos or images that are embarrassing or harmful, then use them to harass or blackmail victims.

How to identify manipulated content

As of now, AI tools to detect deepfakes are still developing. Until reliable detection technology becomes widely available, individuals and businesses should learn to spot common signs of fake content.

Watch for:

• Unnatural blinking or lack of eye movement

• Blurry edges around the face

• Overly smooth or plastic-looking skin

• Lip sync issues or mismatched voice intonation

• Robotic or awkward speech patterns

• A general feeling that something is “off”

Training your staff to recognize these signs can be a powerful first line of defense.

Deepfake detection software

As deepfake technology becomes more advanced, detecting deepfakes has become a significant challenge for individuals and organizations alike. Deepfake detection software leverages machine learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze digital content for signs of manipulation. These algorithms are trained to spot subtle inconsistencies in speech patterns, facial movements, and body language that may indicate the presence of a deepfake. For example, they can detect unnatural blinking, mismatched lip movements, or irregularities in audio recordings that are difficult for the human eye or ear to catch.

In addition to analyzing visual and audio cues, deepfake detection software can examine the metadata of a video or image to verify its authenticity. Tech companies and researchers are constantly refining these tools to keep pace with the rapid evolution of deepfake techniques, aiming to stay one step ahead of malicious deepfakes. The development of effective deepfake detection solutions is crucial for identifying manipulated content and protecting businesses, public figures, and the general public from the dangers posed by deceptive media.

How to protect your business from deepfakes

Train your employees

Make deepfake awareness part of your cybersecurity training. Teach employees to verify unusual requests, especially those involving financial transactions. For example, if someone receives a phone call from a company executive asking to transfer a large sum of money, they should confirm the request through a second communication channel or by using pre-agreed verification steps.

Monitor your online presence

Regularly check social media and video platforms for fake content involving your brand or your leadership team. If you detect a fake video or audio clip, respond quickly and transparently to minimize reputational damage.

Communicate openly

If your company becomes the victim of a deepfake, inform your stakeholders immediately. A strong public relations response can reduce confusion and stop misinformation from spreading. Denying or ignoring the incident may only increase speculation.

The legal landscape

Currently, legal protections against deepfakes are limited, and the deepfakes legal landscape is still evolving. Regulating deepfake content presents significant challenges, and there is a growing need for legal measures to address malicious or nonconsensual deepfake use. However, proposed legislation like the Deepfakes Accountability Act seeks to address this by requiring creators to disclose altered media. If passed, it would make it a crime to create and distribute deepfakes without clear labeling.

Stronger legal frameworks are necessary to protect victims and ensure accountability, especially as deepfakes become easier to produce.

The future of deepfakes

Looking ahead, the future of deepfakes presents both exciting opportunities and serious risks. On the positive side, deepfake technology has the potential to transform content creation, enabling businesses to create personalized videos, realistic fake images, and innovative audio recordings for marketing, education, and entertainment. However, the same technology can be exploited for harmful purposes, such as spreading false information, inciting hate speech, or perpetrating large scale financial fraud.

As deepfake technology continues to advance, the need for effective technological solutions – such as deepfake detection software – will only grow. Raising awareness about the risks and consequences of deepfakes is essential, as is adapting existing laws and regulations to address the unique challenges posed by synthetic media. By staying informed and proactive, businesses and individuals can harness the benefits of deepfake technology while minimizing its potential for misuse. Ultimately, the future of deepfakes will depend on our collective ability to balance innovation in digital content with the responsibility to protect society from the dangers of manipulated media.

Final thoughts

Understanding what a deepfake is is no longer optional. For businesses, the threat is real. A single manipulated video or audio file can damage reputations, disrupt operations, or cost millions in financial losses.

Technological tools will continue to improve, but the most effective defense is education, awareness, and preparation. By training employees in awareness training, monitoring your brand, and responding quickly to incidents, you can protect your business in an era where not everything you see – or hear – can be trusted.