集群式深度伪造信息攻击情报感知方法研究

Research on Information Perception Methods for Clustered Deepfake Information Attacks

[目的/意义]AIGC背景产物之一的集群式深度伪造信息由于主观恶性及难发现、难识别与难控制特点而易引发剧烈现实危害,其攻击情报感知尤为重要。[方法/过程]以GJK算法为基座设计深度伪造信息识别方案、以曼哈顿距离为依据建立集群式特征分析模型、以画像技术为框架提供情报结构化体系,实现攻击意图判定下的感知方法设计。为检验方法有效性,将其纳入以兵棋推演为支撑、以CMO软件为工具的仿真过程,在环境配置、交互式想定单元给定与应用匹配基础上予以执行。[结果/结论]仿真结果表明,方法设计能够有效实现深度伪造信息识别、集群式特征分析与情报画像提取功能。为使研究发挥更大效益,结合仿真结果探讨情报感知能力建设策略。[创新/局限]创新在于提供一套完整的集群式深度伪造信息攻击情报感知方法;不足在于,内容侧重于方法研究,未对理论问题进行过多论述。

[Purpose/significance]One of the background products of AIGC,cluster based deepfake information,is prone to severe real-world harm due to its subjective malignancy,difficulty in detection,identification,and control,making its attack in-formation perception particularly important.[Method/process]Design a deepfake information recognition scheme based on the GJK algorithm,establish a cluster feature analysis model based on Manhattan distance,and provide a structured intelligence system u-sing portrait technology as a framework to achieve perception method design under attack intent determination.To verify the effective-ness of the method,it is incorporated into a simulation process supported by wargame deduction and using CMO software as a tool,and executed based on environmental configuration,interactive scenario unit assignment,and application matching.[Result/con-clusion]The perception results show that the method design can effectively achieve deepfake information recognition,cluster feature analysis,and intelligence portrait extraction functions.To maximize the effectiveness of research,we will explore strategies for building information perception capabilities based on deduction results.[Innovations/limitations]Innovation lies in providing a complete set of clusters based deepfake information attack information perception methods.The shortcoming is that the content focu-ses on methodological research and does not specifically discuss the theoretical issues involved.