With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in the future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics.

翻译：随着深度学习（DL）模型在社会几乎所有方面的大规模应用，一系列独特的挑战随之而来。这些风险主要围绕这些模型的架构，构成了重大挑战，而应对这些挑战对于其未来的成功实施和使用至关重要。在本研究中，我们阐述了当前部署到生产环境中的DL模型所面临的安全挑战，并基于计算、人工智能和硬件技术的进步，预测了未来DL技术可能遇到的挑战。此外，我们提出了风险缓解技术以抑制这些挑战，并提供了度量评估来衡量这些指标的有效性。