Fast Facial Landmark Detection and Applications: A Survey

In this paper we survey and analyze modern neural-network-based facial landmark detection algorithms. We focus on approaches that have led to a significant increase in quality over the past few years on datasets with large pose and emotion variability, high levels of face occlusions - all of which are typical in real-world scenarios. We summarize the improvements into categories, provide quality comparison on difficult and modern in-the-wild datasets: 300-W, AFLW, WFLW, COFW. Additionally, we compare algorithm speed on CPU, GPU and Mobile devices. For completeness, we also briefly touch on established methods with open implementations available. Besides, we cover applications and vulnerabilities of the landmark detection algorithms. Based on which, we raise problems that as we hope will lead to further algorithm improvements in future.

Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping

Neural networks are now actively being used for computer vision tasks in security critical areas such as robotics, face recognition, autonomous vehicles yet their safety is under question after the discovery of adversarial attacks. In this paper we develop simplified adversarial attack algorithms based on a scoping idea, which enables execution of fast adversarial attacks that minimize structural image quality (SSIM) loss, allows performing efficient transfer attacks with low target inference network call count and opens a possibility of an attack using pen-only drawings on a paper for the MNIST handwritten digit dataset. The presented adversarial attack analysis and the idea of attack scoping can be easily expanded to different datasets, thus making the paper's results applicable to a wide range of practical tasks.