In recent past years, Deep Learning presented an excellent performance in different areas like image recognition, pattern matching, and even in cybersecurity. The Deep Learning has numerous advantages including fast solving complex problems, huge automation, maximum application of unstructured data, ability to give high quality of results, reduction of high costs, no need for data labeling, and identification of complex interactions, but it also has limitations like opaqueness, computationally intensive, need for abundant data, and more complex algorithms. In our daily life, we used many applications that use Deep Learning models to make decisions based on predictions, and if Deep Learning models became the cause of misprediction due to internal/external malicious effects, it may create difficulties in our real life. Furthermore, the Deep Learning training models often have sensitive information of the users and those models should not be vulnerable and expose security and privacy. The algorithms of Deep Learning and machine learning are still vulnerable to different types of security threats and risks. Therefore, it is necessary to call the attention of the industry in respect of security threats and related countermeasures techniques for Deep Learning, which motivated the authors to perform a comprehensive survey of Deep Learning security and privacy security challenges and countermeasures in this paper. We also discussed the open challenges and current issues.

中文翻译：

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深度学习安全性和隐私防御技术综述

在过去的几年中，深度学习在图像识别，模式匹配乃至网络安全等不同领域均表现出色。深度学习具有许多优势，包括快速解决复杂问题，自动化程度高，非结构化数据的最大应用，能够提供高质量结果，降低高成本，无需数据标签以及识别复杂交互的能力，但它还具有局限性，例如不透明，计算量大，需要大量数据以及更复杂的算法。在我们的日常生活中，我们使用了许多使用深度学习模型的应用程序来基于预测做出决策，并且如果深度学习模型由于内部/外部恶意影响而成为错误预测的原因，则可能会给我们的现实生活带来困难。此外，深度学习训练模型通常具有用户的敏感信息，并且这些模型不应易受攻击并暴露安全性和隐私性。深度学习和机器学习的算法仍然容易受到不同类型的安全威胁和风险的攻击。因此，有必要引起业界对于深度学习的安全威胁和相关对策技术的关注，这促使作者对深度学习安全和隐私安全的挑战与对策进行全面的调查。我们还讨论了公开挑战和当前问题。深度学习和机器学习的算法仍然容易受到不同类型的安全威胁和风险的攻击。因此，有必要引起业界对于深度学习的安全威胁和相关对策技术的关注，这促使作者对深度学习安全和隐私安全的挑战与对策进行全面的调查。我们还讨论了公开挑战和当前问题。深度学习和机器学习的算法仍然容易受到不同类型的安全威胁和风险的攻击。因此，有必要引起业界对于深度学习的安全威胁和相关对策技术的关注，这促使作者对深度学习安全和隐私安全的挑战与对策进行全面的调查。我们还讨论了公开挑战和当前问题。