论文标题
深度学习中对神经特洛伊木马攻击和防御的调查
A Survey of Neural Trojan Attacks and Defenses in Deep Learning
论文作者
论文摘要
人工智能（AI）在很大程度上依赖于深度学习 - 即使在安全至关重要的高风险领域，这种技术在AI的现实应用中变得越来越流行。但是，最近发现可以通过将特洛伊木马嵌入其中来操纵深度学习。不幸的是，务实的解决方案旨在规避深度学习的计算要求，例如将模型培训或数据注释外包给第三方，进一步增加了模型对特洛伊木马攻击的敏感性。由于该主题在深度学习中的关键重要性，最近的文献在这个方向上看到了许多贡献。我们对设计特洛伊木马攻击的技术进行深入学习和探索他们的防御能力进行了全面审查。我们的信息调查系统地组织了最新文献，并讨论了方法的关键概念，同时假设对读者部分的领域了解最少。它为更广泛的社区提供了一个可理解的门户，以了解神经木马的最新发展。
Artificial Intelligence (AI) relies heavily on deep learning - a technology that is becoming increasingly popular in real-life applications of AI, even in the safety-critical and high-risk domains. However, it is recently discovered that deep learning can be manipulated by embedding Trojans inside it. Unfortunately, pragmatic solutions to circumvent the computational requirements of deep learning, e.g. outsourcing model training or data annotation to third parties, further add to model susceptibility to the Trojan attacks. Due to the key importance of the topic in deep learning, recent literature has seen many contributions in this direction. We conduct a comprehensive review of the techniques that devise Trojan attacks for deep learning and explore their defenses. Our informative survey systematically organizes the recent literature and discusses the key concepts of the methods while assuming minimal knowledge of the domain on the readers part. It provides a comprehensible gateway to the broader community to understand the recent developments in Neural Trojans.
