IEEE

Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples

Abstract: We introduce Adversarial Sparse Teacher (AST), a robust defense method against distillation-based model stealing attacks. Our approach trains a teacher model using adversarial examples to ...
IEEE

Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples

Abstract: Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs ...