Deep Learning Attacks and Active Defences: A Cybersecurity Perspective

深度学习技术对于经济发展、军事控制和战略竞争力至关重要的信念加速了它在地球仪的发展。然而，经验揭示了一个令人失望的事实，即深度学习模型容易受到一系列安全攻击。因此，将设计一系列方法和防御策略，使深度学习系统能够抵御这些攻击。这些方法需要分析攻击的生命周期，以便在早期阶段识别它们。有了这些知识，就可以制定积极的防御方法和法医战略，以确保有效的防御和防止进一步的攻击。此外，输出将可推广到大多数深度学习服务。

The belief that deep learning technology is imperative for economic development, military control, and strategic competitiveness has accelerated its development across the globe. However, experience has revealed the disappointing fact that deep learning models are vulnerable to a range of security attacks. Hence, a series of methodologies and defence strategies will be devised that make deep learning systems robust to these attacks. The methodologies require analysing attack lifecycles to identify them in their early stages. With this knowledge, active defence methods and forensic strategies can be developed to ensure efficient defences and prevent further attacks. Moreover, the outputs will be generalisable to most deep learning services.