The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media. Recently, researchers turned to use adversarial examples as a countermeasure. In this paper, we assess the effectiveness of using two widely known adversarial methods (BIM and ILLC) for de-identifying personal images. We discovered, unlike previous claims in the literature, that it is not easy to get a high protection success rate (suppressing identification rate) with imperceptible adversarial perturbation to the human visual system. Finally, we found out that the transferability of adversarial examples is highly affected by the training parameters of the network with which they are generated.

The government of Morocco is looking for a contractor to install facial recognition systems in that nation's Rabat-Sale Airport. It reportedly would be the first such facility in the nation to have face biometrics. Officials want a One ID biometric system in a new terminal. A tender notification (103-22-A00) was published this week; it closes September 15. According to the Morocco World News, the National Airports Office has received a MAD363 million (approximately US$37 million) loan to upgrade Rabat-Sale.

Advancing research in facial anti-spoofing is not possible without data. Big data is paramount to building a powerful facial liveness detector because convolutional neural networks have millions of parameters, and the optimization process is a bit tricky. The well-trained network is composed of properly configured parameters. When we feed the input image into this function, it returns the class of input indicating whether it is spoof or not. To train a detector, we collect a set of photos or videos of many live people and a set of spoofs.