Detection and Continual Learning of Novel Face Presentation Attacks

By Mohammad Rostami, Leonidas Spinoulas, Mohamed E. Hussein, Joe Mathai, Wael AbdAlmageed. 2021

56 citations recorded by the source corpus.

Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research. However, state-of-the-art face antispoofing systems are still vulnerable to novel types of attacks that are never seen during training. Moreover, even if such attacks are correctly detected, these systems lack the ability to adapt to newly encountered attacks. The post-training ability of continually detecting new types of attacks and self-adaptation to identify these attack types, after the initial detection phase, is highly appealing. In this paper, we enable a deep neural network to detect anomalies in the observed input data points as potential new types of attacks by suppressing the confidence-level of the network outside the training samples’ distribution. We then use experience replay to update the model to incorporate knowledge about new types of attacks without forgetting the past learned attack types. Experimental results are provided to demonstrate the effectiveness of the proposed method on two benchmark datasets as well as a newly introduced dataset which exhibits a large variety of attack types. 1

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability, Meta & Continual Learning, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hang Su
2. Jun Zhu
3. Liyuan Wang
4. Xingxing Zhang
5. Yinpeng Dong
6. Vamsi Krishna Ithapu
7. Alan L. Yuille
8. Tianyu Pang

In Safety, Robustness & Interpretability

1. Hang Su
2. Jun Zhu
3. Yinpeng Dong
4. Tianyu Pang
5. Vamsi Krishna Ithapu

In Meta & Continual Learning

1. Xingxing Zhang
2. Liyuan Wang
3. Hang Su
4. Jun Zhu
5. Simone Scardapane
6. Valerio Marsocci
7. Liyuan Wang
8. Hongwei Yan

In Signal Processing

1. Jun Zhu
2. Hang Su
3. Xingxing Zhang
4. Takaaki Hori

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “NACL [367] enables face antispoofing systems to detect potential novel attacks and remember the old ones with experience replay.”