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New Threats Against Object Detector with Non-local Block

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

Abstract

The introduction of non-local blocks to the traditional CNN architecture enhances its performance for various computer vision tasks by improving its capabilities of capturing long-range dependencies. However, the usage of non-local blocks may also introduce new threats to computer vision systems. Therefore, it is important to study the threats caused by non-local blocks before directly applying them on commercial systems. In this paper, two new threats named disappearing attack and appearing attack against object detectors with a non-local block are investigated. The former aims at misleading an object detector with a non-local block such that it is unable to detect a target object category while the latter aims at misleading the object detector such that it detects a predefined object category, which is not present in images. Different from the existing attacks against object detectors, these threats are able to be performed in long range cases. This means that the target object and the universal adversarial patches learned from the proposed algorithms can have long distance between them. To examine the threats, digital and physical experiments are conducted on Faster R-CNN with a non-local block and 6331 images from 56 videos. The experiments show that the universal patches are able to mislead the detector with greater probabilities. To explain the threats from non-local blocks, the reception fields of CNN models with and without non-local blocks are studied empirically and theoretically.

Keywords

Non-local block Adversarial examples Object detection 

Notes

Acknowledgements

This work is partially supported by the Ministry of Education, Singapore through Academic Research Fund Tier 1, RG30/17.

Supplementary material

504476_1_En_29_MOESM1_ESM.pdf (2 mb)
Supplementary material 1 (pdf 2015 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Nanyang Technological UniversitySingaporeSingapore

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