Face recognition systems use the templates (extracted from users' face images) stored in the
system's database for recognition. In a template inversion attack, the adversary gains access …

Face recognition based on deep convolutional neural networks (CNN) shows superior
accuracy performance attributed to the high discriminative features extracted. Yet, the …

In this article, we comprehensively evaluate the vulnerability of state-of-the-art face
recognition systems to template inversion attacks using 3D face reconstruction. We propose …

The challenge posed by the template-based reconstruction attack significantly impacts the
security and privacy of biometric systems. Current reconstruction techniques rely on …

In this paper, we focus on the template inversion attack against face recognition systems and
propose a new method to reconstruct face images from facial templates. Within a generative …

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural
networks (CNNs) to extract deep features, called embeddings, from face images. The face …

Face recognition systems are increasingly being used in different applications. In such
systems, some features (also known as embeddings or templates) are extracted from each …

Advances in deep learning and convolutional neural networks (ConvNets) have driven
remarkable face recognition (FR) progress recently. However, the black-box nature of …

In this paper, we use synthetic data and propose a new method for template inversion
attacks against face recognition systems. We use synthetic data to train a face reconstruction …

Deep neural networks are increasingly employed to create adversarial face images, aiming
to deceive face recognition systems. While the majority of studies concentrate on digital …