USE OF ARTIFICIAL INTELLIGENCE TO ATTACK BIOMETRIC ACCESS CONTROLS

Using Generative AI to bypass or provide false information to a biometric system involves creating output like an image of a face or a spoofed recording of someone's voice, intending to trick the system into accepting it as authentic biometric information. The use of AI-generated artifacts for malicious purposes can significantly reduce the cost of conducting sophisticated attacks on biometric systems. Generative AI has made it easier to spoof real people's biometric traits with higher quality and on a larger scale. ?

Presentation Attack

AI can be used to generate realistic synthetic biometric data to fool authentication systems:

• Deepfake fingerprints or facial images can be created to mimic legitimate users
• AI algorithms can produce high-quality spoofing materials that evade detection mechanisms

Adversarial Attacks

Hackers can use AI to subtly alter biometric samples in ways that cause misclassification:

• Small changes to facial images or fingerprints can trick the system into false accepts/rejects
• This exploits vulnerabilities in the AI models used for biometric matching

Face-Swap Injection Attacks

There has been a massive rise in the use of AI-generated face-swap videos to bypass facial recognition systems:

• 704% increase in face-swap injection attacks in the second half of 2023
• Easily accessible face-swapping tools make this attack method widely available
• Attackers use emulators to disguise virtual cameras and trick liveness detection

Biometric Data Reconstruction

AI techniques can be used to reverse-engineer stolen biometric data:

• Image reconstruction and template regeneration allow hackers to recreate biometric samples
• This enables the impersonation of legitimate users

Model Poisoning

The AI models used in biometric systems can be compromised during training:

• Injecting malicious data can introduce biases or weaknesses
• This allows attackers to exploit the compromised models

While these AI-enabled attacks pose serious threats, researchers are also developing AI-powered defenses like advanced liveness detection and adversarial training to counter them. Multimodal biometrics and continuous system updates are other strategies to improve security against AI-driven biometric hacks.

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