Revisiting Pixel-Wise Supervision for Face Anti-Spoofing

Anti-spoofing, Face

Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues....

Revisiting Robust Visual Tracking Using Pixel-Wise Posteriors

In this paper we present an in-depth evaluation of a recently published tracking algorithm [6] which intelligently couples rigidregistration and color-based segmentation using level-sets. The original method did not arouse the deserved interest in the community, most likely due to challenges in reimplementation and the lack of a quantitative evaluation. Therefore, we reimplemented this baseline...

Learn Convolutional Neural Network for Face Anti-Spoofing

Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some...

Revisiting Email Spoofing Attacks

The email system is the central battleground against phishing and social engineering attacks, and yet email providers still face key challenges to authenticate incoming emails. As a result, attackers can apply spoofing techniques to impersonate a trusted entity to conduct highly deceptive phishing attacks. In this work, we study email spoofing to answer three key questions: (1) How do email pro...