Biometric security is the cornerstone of modern identity verification and
authentication systems, where the integrity and reliability of biometric
samples is of paramount importance. This paper introduces AttackNet, a bespoke
Convolutional Neural Network architecture, meticulously designed to combat
spoofing threats in biometric systems. Rooted in deep learning methodologies,
this model offers a layered defense mechanism, seamlessly transitioning from
low-level feature extraction to high-level pattern discernment. Three
distinctive architectural phases form the crux of the model, each underpinned
by judiciously chosen activation functions, normalization techniques, and
dropout layers to ensure robustness and resilience against adversarial attacks.
Benchmarking our model across diverse datasets affirms its prowess, showcasing
superior performance metrics in comparison to contemporary models. Furthermore,
a detailed comparative analysis accentuates the model's efficacy, drawing
parallels with prevailing state-of-the-art methodologies. Through iterative
refinement and an informed architectural strategy, AttackNet underscores the
potential of deep learning in safeguarding the future of biometric security.

AttackNet 是一种巧妙设计的卷积神经网络架构，采用深度学习方法应对生物特征系统中的欺骗威胁，通过低级特征提取到高级模式识别无缝过渡的分层防御机制，具有优越的性能指标，并与当代模型进行了详细的比较分析，突出了深度学习在生物特征安全领域的潜力。