Which of the following would BEST help to prevent the compromise of a facial recognition AI system through the use of alterations in facial appearance?

The correct answer is A. Enhancing training data to increase variance. AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly…

AI Security Design and Implementation

Question

Options

AEnhancing training data to increase variance
BMonitoring the system for misuse cases
CFine-tuning the AI model to decrease hallucinations
DImplementing a secondary AI system to confirm images

How the community answered

(26 responses)

A
58% (15)
B
27% (7)
C
12% (3)
D
4% (1)

Explanation

AAISM materials note that adversaries may attempt to bypass facial recognition by disguising or altering appearance. The most effective mitigation is to enhance training data with a wide range of variances in facial features, lighting, and disguises so the system can robustly detect authentic users despite adversarial attempts. Monitoring and secondary confirmation are supportive controls but are reactive. Fine-tuning to reduce hallucinations is irrelevant in this context, as hallucinations apply more to generative AI. The best preventive measure is strengthening the model with diverse, variance-rich training data.

Topics

#AI Model Robustness#Adversarial Attack Prevention#Training Data Enhancement#Facial Recognition Security

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