标题： 深度学习中的架构后门：漏洞、检测与防御的综述 显示英文标题

标题： Architectural Backdoors in Deep Learning: A Survey of Vulnerabilities, Detection, and Defense

摘要： 架构后门对深度神经网络构成了一种未被充分研究但至关重要的威胁，它将恶意逻辑直接嵌入到模型的计算图中。 与传统的数据污染或参数操作不同，架构后门可以规避标准缓解技术，并在干净重训练后仍然存在。 本综述系统地整合了关于架构后门的研究，涵盖了编译器级别的操作、受污染的AutoML流程以及供应链漏洞。 我们评估了新兴的检测和防御策略，包括静态图检查、动态模糊测试和部分形式化验证，并指出了它们在面对分布式或隐蔽触发器时的局限性。 尽管最近取得了一些进展，但可扩展且实用的防御措施仍然难以实现。 最后，我们列出了开放性的挑战，并提出了加强供应链安全、加密模型认证和下一代基准的方向。 本综述旨在引导未来的研究，以全面应对深度学习系统中的结构后门威胁。 显示英文摘要

摘要： Architectural backdoors pose an under-examined but critical threat to deep neural networks, embedding malicious logic directly into a model's computational graph. Unlike traditional data poisoning or parameter manipulation, architectural backdoors evade standard mitigation techniques and persist even after clean retraining. This survey systematically consolidates research on architectural backdoors, spanning compiler-level manipulations, tainted AutoML pipelines, and supply-chain vulnerabilities. We assess emerging detection and defense strategies, including static graph inspection, dynamic fuzzing, and partial formal verification, and highlight their limitations against distributed or stealth triggers. Despite recent progress, scalable and practical defenses remain elusive. We conclude by outlining open challenges and proposing directions for strengthening supply-chain security, cryptographic model attestations, and next-generation benchmarks. This survey aims to guide future research toward comprehensive defenses against structural backdoor threats in deep learning systems.