Proceedings ArticleDOI
Crafting A Panoptic Face Presentation Attack Detector
Suril Mehta,Anannya Uberoi,Akshay Agarwal,Mayank Vatsa,Richa Singh +4 more
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TLDR
This paper designs a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF) and shows superior performance in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database.Abstract:
With the advancements in technology and growing popularity of facial photo editing in the social media landscape, tools such as face swapping and face morphing have become increasingly accessible to the general public. It opens up the possibilities for different kinds of face presentation attacks, which can be taken advantage of by impostors to gain unauthorized access of a biometric system. Moreover, the wide availability of 3D printers has caused a shift from print attacks to 3D mask attacks. With increasing types of attacks, it is necessary to come up with a generic and ubiquitous algorithm with a panoptic view of these attacks, and can detect a spoofed image irrespective of the method used. The key contribution of this paper is designing a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF). The performance is evaluated for digital and physical attacks in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database. Experimental results showcase the superior performance of the proposed presentation attack detection algorithm.read more
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Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Anjith George,Sébastien Marcel +1 more
TL;DR: In this paper, a one-class Gaussian Mixture model is used to learn a compact embedding for bonafide class while being far from the representation of attacks, and the proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks.
Proceedings ArticleDOI
CHIF: Convoluted Histogram Image Features for Detecting Silicone Mask based Face Presentation Attack
TL;DR: This research proposes a computationally efficient solution by utilizing the power of CNN filters, and texture encoding for silicone mask based presentation attacks by binarizing the image region after convolving the region with the filters learned via CNN operations.
Posted Content
On Improving Temporal Consistency for Online Face Liveness Detection
Xiang Xu,Yuanjun Xiong,Wei Xia +2 more
TL;DR: This paper focuses on improving the online face liveness detection system to enhance the security of the downstream face recognition system, and proposes a simple yet effective solution based on temporal consistency that is more robust against several presentation attacks in various scenarios.
Posted Content
MixNet for Generalized Face Presentation Attack Detection
TL;DR: This research has proposed a deep learning-based network termed as MixNet to detect presentation attacks in cross-database and unseen attack settings and shows the effectiveness of the proposed algorithm.
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On the Robustness of Face Recognition Algorithms Against Attacks and Bias
TL;DR: In this paper, different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed.
References
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