The online education and certifications industry is set to continue its rapid adoption and expansion with the number of online learning users expected to increase to 57 million by 2027 alone. However, deepfake technology has also been evolving significantly and poses an impactful threat to the integrity of these platforms, as malicious actors can use deepfakes to do things like cheat, deceive users and/or educational institutions, and commit fraud against eLearning platforms. To defend against these threats, biometric authentication technology can be applied in several ways. In this article, we explore how deepfakes are affecting the online education and certifications industry and how platforms can apply biometrics to help defend against these generative AI threats.

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 Online Learning and Certifications?

Deepfakes are fooling people around the globe, but some specific ways that these threats might impact the online learning and certification industry includes:

1. Cheating and Fraud: Deepfakes could be used for cheating and fraud in online exams and certifications. For example, a student could use a deepfake to impersonate someone else before and/or during an exam, allowing them to pass the test fraudulently. This could devalue the certification and undermine the credibility of the online learning platform.
2. Manipulation of Course Content: Malicious actors could use deepfakes to manipulate course content, such as altering lectures or tutorials to convey false information. This could lead to misunderstandings among students and impact their learning outcomes.
3. Fake Instructional Videos: Similar to number two, malicious actors could also use deepfakes to create entirely new, fake instructional videos. By impersonating instructors or experts, they could disseminate false information, mislead students, and damage the reputation of legitimate educators.
4. Identity Theft: Deepfakes could also be used for identity theft, where a malicious actor creates a fake video or audio impersonating a student or instructor. This could be used to gain unauthorized access to courses or certifications, potentially leading to financial loss or reputational damage.
5. Damage to Reputation: Last but certainly not least, the proliferation of deepfakes in online learning could damage the reputation of legitimate educators and institutions. If deepfakes are used to create fake videos or audio impersonating reputable instructors, it could erode trust in the online learning industry as a whole.

To mitigate the threat of deepfakes in online learning and certifications, it is essential for online education platforms and institutions to implement robust authentication and verification measures. This could include biometric authentication, such as facial recognition, to verify the identity of students and instructors.

Additionally, educating students and instructors about the dangers of deepfakes and how to spot them can help reduce the impact of malicious actors. By staying vigilant and implementing proactive measures, the online learning and certifications industry can protect itself against the potential threats posed by deepfakes.

How Does Biometrics Defend Against Deepfakes in Online Learning and Certifications?

As mentioned above, biometric authentication technology offers a 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.