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Found 5 papers, 0 papers with code
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition
RADAP employs innovative techniques, such as FCutout and F-patch, which use Fourier space sampling masks to improve the occlusion robustness of the FR model and the performance of the patch segmenter.
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns.
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models.
RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer
Furthermore, we propose a random meta-optimization strategy for ensembling several pre-trained face models to generate more general adversarial masks.
RandoMix: A mixed sample data augmentation method with multiple mixed modes
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains.
