在大数据时代，人工智能得到了蓬勃发展，尤其以机器学习、深度学习为代表的技术更是取得了突破性进展.随着人工智能在实际场景中的广泛应用，人工智能的安全和隐私问题也逐渐暴露出来，并吸引了学术界和工业界的广泛关注.以机器学习为代表，许多学者从攻击和防御的角度对模型的安全问题进行了深入的研究，并且提出了一系列的方法.然而，当前对机器学习安全的研究缺少完整的理论架构和系统架构.从训练数据逆向还原、模型结构反向推演、模型缺陷分析等角度进行了总结和分析，建立了反向智能的抽象定义及其分类体系.同时，在反向智能的基础上，将机器学习安全作为应用对其进行简要归纳.最后探讨了反向智能研究当前面临的挑战以及未来的研究方向.建立反向智能的理论体系，对于促进人工智能健康发展极具理论意义.

In the era of big data, artificial intelligence, especially the representative technologies of machine learning and deep learning, has made great progress in recent years. As artificial intelligence has been widely used to various real-world applications, the security and privacy problems of artificial intelligence is gradually exposed, and has attracted increasing attention in academic and industry communities. Researchers have proposed many works focusing on solving the security and privacy issues of machine learning from the perspective of attack and defense. However, current methods on the security issue of machine learning lack of the complete theory framework and system framework. This survey summarizes and analyzes the reverse recovery of training data and model structure, the defect of the model, and gives the formal definition and classification system of reverse-engineering artificial intelligence. In the meantime, this survey summarizes the progress of machine learning security on the basis of reverse-engineering artificial intelligence, where the security of machine learning can be taken as an application. Finally, the current challenges and future research directions of reverse-engineering artificial intelligence are discussed, while building the theory framework of reverse-engineering artificial intelligence can promote the develop of artificial intelligence in a healthy way.

国家自然科学基金优秀青年科学基金（62122013）；国家自然科学基金广东联合基金重点项目（U2001211）；北京理工大学青年教师学术启动计划（3070012222010）