Fine-tuning face anti-spoofing models: exploring the transformative impact of image-level biometric privacy-enhancing techniques (2024)
Face Anti-Spoofing (FAS) systems are entrusted with the task of determining whether the content of a facial image is genuine or fake. The performance and overall quality of those systems depend on the data they are fed during their development stage, meaning that high-resolution and diverse facial datasets are commonly used. Even though FAS systems are particularly useful in some situations, they do raise concerns in regards to the privacy of the individuals whose images are used to train them. The straightforward solution is to apply filters to the facial images, at the expense of the systems’ performance, since recognising faces becomes increasingly harder. This is where Biometric Privacy-Enhancing Techniques (B-PETs) come into play, which help to alleviate the adversarial tension between biometric utility and privacy gains. This work concerned itself with assessing the impact of 3 distinct image-level B-PETs in the performance of 3 architecturally distinct FAS systems and found that image-level B-PETs are not fit for finding a valuable trade-off, suggesting that more sophisticated techniques are needed.