Deep Learning Architectures for Safe and Secure Artificial Intelligence

Abstract

Abstract: Deep learning has become a cornerstone of artificial intelligence (AI), driving advances in areas like computer vision, natural language processing, and autonomous systems. However, as these models become more powerful, the need for safety and security becomes paramount. This paper explores the architectural innovations in deep learning aimed at ensuring the safe and secure deployment of AI systems. It reviews the latest developments in adversarial training, robust optimization, and model interpretability to counteract vulnerabilities such as adversarial attacks and data poisoning. Adversarial training techniques, which involve training models to withstand crafted input manipulations, play a crucial role in improving the resilience of deep learning models. Additionally, the paper delves into privacy-preserving techniques such as federated learning and differential privacy, which allow models to learn from distributed data sources without compromising sensitive information. It also evaluates the role of explainable AI (XAI) methods in making deep learning models more transparent, thereby enhancing trust among users and stakeholders. This study is based on a systematic review of recent research findings and real-world applications, offering insights into how deep learning architectures can be optimized for both safety and security. The aim is to provide a roadmap for researchers and practitioners looking to build more robust AI systems. Ultimately, the paper underscores the importance of balancing performance with safety and security in the design of future deep learning models, ensuring they can be deployed reliably in critical environments.