Face Anti-Spoofing
66 papers with code • 8 benchmarks • 17 datasets
Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Some examples of attacks:
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Print attack: The attacker uses someone’s photo. The image is printed or displayed on a digital device.
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Replay/video attack: A more sophisticated way to trick the system, which usually requires a looped video of a victim’s face. This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.
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3D mask attack: During this type of attack, a mask is used as the tool of choice for spoofing. It’s an even more sophisticated attack than playing a face video. In addition to natural facial movements, it enables ways to deceive some extra layers of protection such as depth sensors.
( Image credit: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing )
Benchmarks
These leaderboards are used to track progress in Face Anti-Spoofing
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Libraries
Use these libraries to find Face Anti-Spoofing models and implementationsDatasets
Replay-Attack
MSU-MFSD
CASIA-MFSD
SiW
CASIA-FASD
CelebA-Spoof
CASIA-SURF
Replay-Mobile
HiFiMask
HQ-WMCA
Latest papers with no code
Presentation Attack Detection using Convolutional Neural Networks and Local Binary Patterns
The second uses a shallow CNN based on a modified Spoofnet architecture, which is trained normally.
Fine-Grained Annotation for Face Anti-Spoofing
In this paper, we propose a fine-grained annotation method for face anti-spoofing.
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images
Single-shot face anti-spoofing (FAS) is a key technique for securing face recognition systems, and it requires only static images as input.
Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing
These scenarios often feature low-quality face images, necessitating the modeling of data uncertainty to improve stability under extreme conditions.
Semi-Supervised learning for Face Anti-Spoofing using Apex frame
Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance.
Saliency-based Video Summarization for Face Anti-spoofing
Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency.
Hyperbolic Face Anti-Spoofing
To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks.
Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach
As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy.
FaceSkin: A Privacy Preserving Facial skin patch Dataset for multi Attributes classification
Human facial skin images contain abundant textural information that can serve as valuable features for attribute classification, such as age, race, and gender.
