Deep Learning Model Security: Threats and Defenses

TL;DR

This survey reviews security threats to deep learning models, including attacks and defenses, and discusses future directions for improving robustness and privacy in AI systems.

Contribution

It provides a comprehensive overview of vulnerabilities, attack methods, defense strategies, and emerging techniques in deep learning security, highlighting current limitations and future research directions.

Findings

Adversarial attacks compromise model integrity.

Defense methods like adversarial training improve robustness.

Emerging techniques aim to enhance security of large AI models.

Abstract

Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flipping, and backdoor attacks, are explored alongside defenses such as adversarial training, differential privacy, and federated learning, highlighting their strengths and limitations. Advanced methods like contrastive and self-supervised learning are presented for enhancing robustness. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models. A balanced approach to performance and security is essential for developing reliable deep learning systems.