计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 110-116.doi: 10.11896/jsjkx.220300024
刘依凡, 欧博, 熊剑琴
LIU Yifan, OU Bo, XIONG Jianqin
摘要： 对抗重编程是一种针对深度神经网络的攻击方法,它通过往输入图像中添加扰动使网络模型执行攻击者指定的任务,能够破坏训练网络模型的合法使用权。研究和设计对抗重编程方法,对于理解此类攻击并设计相应的防御算法有积极意义。探讨对抗扰动添加区域对对抗重编程算法性能的影响,通过使用噪声不可见性函数来评估图像各区域的对抗失真特性,得到掩蔽矩阵,然后自适应地添加对抗扰动,优化对抗重编程任务。实验结果表明,对于当前主流的深度网络分类模型,所提算法能够提高对抗重编程的攻击性能,并提升修改后图像的不可感知性。
中图分类号:
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