论文标题
AI数据中毒攻击:操纵GO的游戏AI
AI Data poisoning attack: Manipulating game AI of Go
论文作者
论文摘要
随着AI在各个领域的广泛使用,人工智能安全问题变得更加重要。在对抗性例子之后,AI数据中毒攻击将是针对AI安全的最威胁性方法。随着在线AI应用程序的持续更新,攻击者可以上传数据污染模型,以实现某些恶意目的。最近,对AI数据中毒攻击的研究大多是练习的,并使用了自我构建的实验环境,因此它不能像对抗性示例攻击一样接近现实。本文的第一个贡献是为上述问题提供解决方案和突破性,并针对针对针对真实企业的数据中毒攻击,在这种情况下:数据中毒对真实的GO GO AI。我们将木马病毒安装到真正的AI中,该病毒操纵AI的行为。这是我们第一次成功地操纵复杂的AI并为AI数据中毒攻击验证方法提供可靠的方法。在本文中构建特洛伊木马的方法可以扩展到其他领域的更实际的算法,例如内容建议,文本翻译和智能对话。
With the extensive use of AI in various fields, the issue of AI security has become more significant. The AI data poisoning attacks will be the most threatening approach against AI security after the adversarial examples. As the continuous updating of AI applications online, the data pollution models can be uploaded by attackers to achieve a certain malicious purpose. Recently, the research on AI data poisoning attacks is mostly out of practice and use self-built experimental environments so that it cannot be as close to reality as adversarial example attacks. This article's first contribution is to provide a solution and a breakthrough for the aforementioned issue with research limitations, to aim at data poisoning attacks that target real businesses, in this case: data poisoning attacks on real Go AI. We install a Trojan virus into the real Go AI that manipulates the AI's behavior. It is the first time that we succeed in manipulating complicated AI and provide a reliable approach to the AI data poisoning attack verification method. The method of building Trojan in this article can be expanded to more practical algorithms for other fields such as content recommendation, text translation, and intelligent dialogue.