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Targeted Attack for Deep Hashing Based Retrieval

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12346)

Abstract

The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval. Specifically, we first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label. Then we design a novel component-voting scheme to obtain an anchor code as the representative of the set of hash codes of objects with the target label, whose optimality guarantee is also theoretically derived. To balance the performance and perceptibility, we propose to minimize the Hamming distance between the hash code of the adversarial example and the anchor code under the \(\ell ^\infty \) restriction on the perturbation. Extensive experiments verify that DHTA is effective in attacking both deep hashing based image retrieval and video retrieval.

Keywords

Targeted attack Deep hashing Adversarial attack Similarity retrieval 

Notes

Acknowledgments

This work is supported in part by the National Key Research and Development Program of China under Grant 2018YFB1800204, the National Natural Science Foundation of China under Grant 61771273, the R&D Program of Shenzhen under Grant JCYJ20180508152204044, the project “PCL Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications (LZC0019)”, the Natutal Sciences and Engineering Research Council of Canada under Grant RGPIN203035-16, and the Canada Research Chairs Program. We also thank vivo and Rejoice Sport Tech. co., LTD. for their GPUs.

Supplementary material

500725_1_En_36_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1513 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina
  2. 2.Peng Cheng LaboratoryPCL Research Center of Networks and CommunicationsShenzhenChina
  3. 3.vivo AI LabShenzhenChina
  4. 4.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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