Today, the rise of deepfake technology threatens a more significant impact to businesses and their customers than just entertaining videos. Malicious actors, ranging from cybercriminals to state-sponsored agents, are finding nefarious applications for deepfakes that have the potential to cause widespread harm. By exploiting the credibility and authenticity of this manufactured media, these bad actors can deceive, manipulate, and defraud organizations and their customers. For businesses with offerings and solutions of all kinds, understanding the ways in which deepfakes can be used against their customers is crucial for developing effective strategies to mitigate their impact and safeguard against their misuse.

What are Deepfakes?

The phrase “deepfake” comes from the combination of the terms “deep learning” and “fake.” While it doesn’t have one universally agreed-upon definition, a deepfake generally means that a person in existing content is replaced with someone else’s likeness. Essentially, a deepfake is content like a photo, audio, or video that has been manipulated by Machine Learning (ML) and Artificial Intelligence (AI) to make it appear to be something that it is not. Check out this article for even more details on deepfakes and how they work.

How Can Deepfakes Impact Your Customers?

While deepfakes have gained attention for their entertainment value and creative potential, they also present serious risks to businesses and their customers. Here are some common ways in which malicious actors can exploit deepfakes:

1. Fraudulent Content: One of the most immediate threats of deepfakes is their potential use in creating fraudulent content. Malicious actors could use deepfakes to impersonate individuals in videos, making it appear as though they are saying or doing things they never did. On a personal or business level, this could be used to spread false information, damage reputations, or even commit fraud.
2. Social Engineering Attacks: Deepfakes could also be used in social engineering attacks, where attackers manipulate individuals into divulging sensitive information or taking harmful actions. For example, a deepfake video could be used to impersonate a CEO instructing an employee to transfer funds to a fraudulent account.
3. Disinformation Campaigns: Deepfakes could be weaponized in disinformation campaigns to spread false information and manipulate public opinion. By creating convincing videos of political figures or other public figures saying or doing things they didn’t, malicious actors could sow chaos and confusion.
4. Identity Theft: Deepfakes could be used to steal someone’s identity, creating fake videos or images that appear to be the individual. This could be used to access sensitive accounts or commit other forms of fraud.
5. Sabotage and Espionage: Deepfakes could also be used for sabotage or espionage purposes. For example, a deepfake video could be used to manipulate a company’s stock price or damage its reputation.

It’s important for individuals and organizations to be aware of these risks and take steps to protect themselves, such as using strong authentication methods (like biometrics) and being cautious of manipulated media. For consumers, this means choosing providers and solutions that consider these generative AI threats and offer robust protection against them. For business leaders, it’s essential to consider these concerns and take the necessary actions to protect customers and the business. One such action includes the integration of biometric authentication technology into existing solutions and offerings.

How Does Biometrics Defend Against Deepfakes?

As mentioned above, biometric authentication technology helps organizations of all shapes and sizes offer powerful defense against deepfake threats by leveraging:

Facial Recognition:

Facial recognition technology is one of the most commonly used biometric authentication methods. By analyzing facial features such as the size and shape of the eyes, nose, and mouth, facial recognition systems can verify a person’s identity with a high degree of accuracy. When applied to deepfake detection, facial recognition technology can help identify inconsistencies in facial features that indicate a video or image has been manipulated.

Voice Recognition:

Voice recognition technology is another important biometric authentication method. By analyzing various aspects of a person’s voice, such as pitch, tone, and cadence, voice recognition systems can verify their identity. In the context of deepfake detection, voice recognition technology can help identify unnatural or inconsistent speech patterns that may indicate a video or audio recording has been manipulated.

Behavioral Biometrics:

Behavioral biometrics involves analyzing patterns in an individual’s behavior, such as typing speed, mouse movements, and swipe patterns on a touchscreen device. These behavioral patterns are unique to each individual and can be used to verify their identity. When applied to deepfake detection, behavioral biometrics can help identify anomalies in user behavior that may indicate a video or image has been manipulated.

Multimodal Biometrics:

Multimodal biometrics involves combining multiple biometric authentication methods to enhance security. By using a combination of facial recognition, voice recognition, and behavioral biometrics, for example, multimodal biometric systems can provide a more robust defense against deepfake threats. By requiring multiple forms of biometric authentication, these systems can make it more difficult for malicious actors to create convincing deepfakes.

Liveness Detection:

Liveness detection is a crucial component of biometric authentication that helps ensure the authenticity of the biometric data being captured. This technology is designed to detect whether a biometric sample, such as a facial image or a voice recording, comes from a live person or from a spoofing attack, such as a deepfake. Liveness detection algorithms analyze various factors, such as the presence of natural movements in a facial image or the presence of physiological signals in a voice recording, to determine whether the biometric data is from a live person.

When it comes to deepfake threats, liveness detection is essential for preventing malicious actors from using static images or pre-recorded videos to spoof biometric authentication systems. By verifying the liveness of the person providing the biometric sample, liveness detection technology helps defend against deepfake attacks and ensures the integrity of the authentication process.