针对性错配对抗攻击：借花献佛塔
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower
摘要：
访问在线视觉搜索引擎意味着共享私人用户内容-查询图像。我们为基于深度学习的检索系统引入了目标失配攻击的概念，以生成对抗图像来隐藏查询图像。生成的图像看起来与用户想要的查询完全不同，但会导致相同或非常相似的检索结果。将攻击转移到完全不可见的网络是一项挑战。我们成功地攻击部分未知的系统，通过设计各种损失函数的对抗图像的建设。这些包括损失函数，例如，用于未知的全局池化操作或未知的检索系统的输入分辨率。我们对标准检索基准进行了攻击评估，并将检索结果与原始图像和对抗图像进行了比较。
英文摘要：
Access to online visual search engines implies sharing of private user content - the query images. We introduce the concept of targeted mismatch attack for deep learning based retrieval systems to generate an adversarial image to conceal the query image. The generated image looks nothing like the user intended query, but leads to identical or very similar retrieval results. Transferring attacks to fully unseen networks is challenging. We show successful attacks to partially unknown systems, by designing various loss functions for the adversarial image construction. These include loss functions, for example, for unknown global pooling operation or unknown input resolution by the retrieval system. We evaluate the attacks on standard retrieval benchmarks and compare the results retrieved with the original and adversarial image.
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。
