Deep Learning Model Security: Threats and Defenses

TL;DR

This survey provides a comprehensive overview of security threats in deep learning, including adversarial examples, data poisoning, model theft, privacy leakage, and backdoor attacks. It analyzes attack mechanisms in black-box and white-box settings, surveys practical attack case studies, and surveys an array of defenses such as adversarial training, differential privacy, federated learning, trusted hardware, and zero-trust concepts. It highlights advanced defenses using contrastive and self-supervised learning, model distillation, and graph neural network security, and concludes with future directions emphasizing automated defenses and security standards for large AI systems. The work underscores the need to balance performance and security to enable reliable, trustworthy DL deployments across safety- and privacy-critical domains.

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.

Figures (1)

• Figure 1: Adversarial Attack Demonstration