深度学习模型面临多种安全威胁，本文系统梳理了攻防机制与应对策略。
Deep Learning Model Security: Threats and Defenses
- 分析对抗样本、数据投毒等攻击手段的实现原理
- 总结对抗训练、差分隐私等防御方法的优缺点
- 适合关注AI安全的开发者和研究者阅读
深度学习已深刻改变人工智能应用，但面临对抗攻击、数据投毒、模型盗用和隐私泄露等关键安全挑战。本文综述这些漏洞的机制及其对模型完整性与机密性的影响。详细探讨了对抗样本、标签翻转和后门攻击等实际攻击方式，以及对抗训练、差分隐私和联邦学习等防御手段的优劣。同时介绍了对比学习与自监督学习等先进方法在提升模型鲁棒性方面的潜力。最后展望未来方向，强调自动化防御、零信任架构及大模型安全问题的重要性。性能与安全的平衡是构建可靠深度学习系统的核心。
原文摘要 · Abstract (English)
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.
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