MagNet: Detecting Digital Presentation Attacks on Face Recognition - Agarwal, Akshay; Singh, Richa; Vatsa, Mayank; Noore, Afzel (Frontiers in Artificial Intelligence, 2021)
Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. While these applications are for entertainment purposes, they can also be used for digitally attacking face (biometric) recognition systems. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a ``Weighted Local Magnitude Pattern'' (WLMP) feature descriptor. We also present a novel database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to anyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.