深度学习模型的黑盒木马：使用非侵入式网络结构和二进制更改
Blackbox Trojanising of Deep Learning Models : Using non-intrusive network structure and binary alterations
摘要：
人工智能的最新进展，即深度学习，提高了其在许多应用中的采用率。有些国家发挥着重要作用，以至于我们的生计严重依赖它们。然而，与所有技术一样，存在恶意行为者可以利用的漏洞。一种形式的利用是将这些技术，本来是好的，变成双重目的的工具，以支持异常行为，如恶意软件木马。作为主动防御的一部分，研究人员正在主动识别这些漏洞，以便随后制定保护措施。这项研究探索了一种新的黑盒木马方法，使用简单的网络结构修改任何深度学习图像分类模型，将良性模型转换为异常模型，只需简单操作权重即可引发特定类型的错误。在这项研究中讨论了保护这种简单的漏洞的发生的建议。这项研究强调了为这些模型提供足够保障的重要性，以便保护人工智能创新和采用的预期利益。
英文摘要：
Recent advancements in Artificial Intelligence namely in Deep Learning has heightened its adoption in many applications. Some are playing important roles to the extent that we are heavily dependent on them for our livelihood. However, as with all technologies, there are vulnerabilities that malicious actors could exploit. A form of exploitation is to turn these technologies, intended for good, to become dual-purposed instruments to support deviant acts like malicious software trojans. As part of proactive defense, researchers are proactively identifying such vulnerabilities so that protective measures could be developed subsequently. This research explores a novel blackbox trojanising approach using a simple network structure modification to any deep learning image classification model that would transform a benign model into a deviant one with a simple manipulation of the weights to induce specific types of errors. Propositions to protect the occurrence of such simple exploits are discussed in this research. This research highlights the importance of providing sufficient safeguards to these models so that the intended good of AI innovation and adoption may be protected.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
