Manipulating Weights in Face-Recognition AI Systems

Feb 1, 2023 ·

AI-based face recognition systems are becoming increasingly prevalent in many aspects of everyday life. However, the accuracy of these systems can be easily manipulated by changing weights within the system. This article outlines how attacking AI-based face recognition is possible and what can be done to stop it.

The main aim of this attack is to disrupt the accuracy of a face recognition system by altering its weights. To do this, an attacker needs access to the trained weights of the system. By changing these weights, the accuracy of the system can be reduced. In practice, an attacker can alter the weights by adding or removing pixels from a person's face or by manipulating the lighting conditions of an image.

This type of attack can have serious implications for security, as it could allow someone to bypass facial recognition systems used in airports or for other security purposes. In addition, it could be used to create fake images that would pass facial recognition tests.

To prevent this type of attack, the authors suggest that robustness measures need to be taken to protect against malicious manipulation of weights in AI-based face recognition systems. Such measures include encryption of training data, audit trails, and secure coding practices. In addition, the authors also suggest that machine learning models should be tested in various conditions to ensure they are robust to different types of manipulations.

Overall, this article provides a detailed description of how manipulating weights in face recognition AI systems is possible, and what steps can be taken to protect against it. By taking robustness measures and testing the model under different conditions, the accuracy of face recognition systems can be maintained.