MsMemoryGAN: A Multiscale Memory GAN for Palm-Vein Adversarial Purification.

Area of Science:

• Biometrics
• Computer Vision
• Artificial Intelligence

Background:

• Deep neural networks (DNNs) excel in vein recognition but are vulnerable to adversarial attacks.
• Adversarial attacks introduce subtle input perturbations, causing DNNs to misclassify vein patterns.
• Existing defense mechanisms often fall short in robustly handling these attacks.

Purpose of the Study:

• To propose MsMemoryGAN, a novel defense model for filtering adversarial perturbations in vein recognition.
• To enhance the robustness of vein recognition systems against sophisticated adversarial attacks.

Main Methods:

• Designed a multiscale memory autoencoder (MsMemoryAE) with a memory module (MM) for learning normal vein patterns at various scales.
• Introduced a learnable similarity metric within the MM (LSMM) to purify input features by retrieving relevant normal patterns.