Face Anti-Spoofing
64 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 )
Libraries
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Latest papers with no code
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing
Specifically, we propose a novel Class Free Prompt Learning (CFPL) paradigm for DG FAS, which utilizes two lightweight transformers, namely Content Q-Former (CQF) and Style Q-Former (SQF), to learn the different semantic prompts conditioned on content and style features by using a set of learnable query vectors, respectively.
Generalized Face Liveness Detection via De-spoofing Face Generator
In this paper, we conduct an Anomalous cue Guided FAS (AG-FAS) method, which leverages real faces for improving model generalization via a De-spoofing Face Generator (DFG).
Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing
Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems.
Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing
In this work, we propose a novel vision transformer referred to as adaptive-avg-pooling based attention vision transformer (AAViT) that uses modules of adaptive average pooling and attention to replace the module of average value computing.
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing
In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment.
TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing
Enhancing the domain generalization performance of Face Anti-Spoofing (FAS) techniques has emerged as a research focus.
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.