论文标题
评估神经网络后门防御
On Evaluating Neural Network Backdoor Defenses
论文作者
论文摘要
深度神经网络（DNNS）在包括审查和安全性在内的各个领域都表现出卓越的表现。但是，最近的研究表明，DNN容易受到后门攻击的影响。过去，有人提出了一些防御能力，以防止DNN免受此类后门袭击。在这项工作中，我们进行了批判性分析，并确定这些现有防御措施中的常见陷阱，准备后门攻击的全面数据库，对现有的防御措施进行并排评估针对该数据库。最后，我们布局一些一般指南，以帮助研究人员将来发展更强大的防御能力，并避免过去的常见错误。
Deep neural networks (DNNs) demonstrate superior performance in various fields, including scrutiny and security. However, recent studies have shown that DNNs are vulnerable to backdoor attacks. Several defenses were proposed in the past to defend DNNs against such backdoor attacks. In this work, we conduct a critical analysis and identify common pitfalls in these existing defenses, prepare a comprehensive database of backdoor attacks, conduct a side-by-side evaluation of existing defenses against this database. Finally, we layout some general guidelines to help researchers develop more robust defenses in the future and avoid common mistakes from the past.
