Cited by 536Adversarial Robustness in Machine LearningDigital Media Forensic DetectionSecurity and Verification in Computing

The DOI matches its Crossref record; 50 of the 59 references it cites were found in a registry or an index.

Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

Open access
Liu, Yunfei, Ma, Xingjun, Bailey, James, Lu, Feng·2020·Lecture Notes in Computer Science

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-030-58607-2_11

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 59 references

Research works printed but not linked: 16

  1. Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012) · arXiv
  2. Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017) · arXiv
  3. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
  4. Evtimov, I., et al.: Robust physical-world attacks on deep learning models. In: CVPR (2018)
  5. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014) · arXiv
  6. Gu, T., Dolan-Gavitt, B., Garg, S.: Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017) · arXiv
  7. Guo, C., Rana, M., Cisse, M., Van Der Maaten, L.: Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017) · arXiv
  8. Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. arXiv preprint arXiv:1907.07174 (2019) · arXiv
  9. Li, S., Zhao, B.Z.H., Yu, J., Xue, M., Kaafar, D., Zhu, H.: Invisible backdoor attacks against deep neural networks. arXiv preprint arXiv:1909.02742 (2019) · arXiv
  10. Li, Y., Zhai, T., Wu, B., Jiang, Y., Li, Z., Xia, S.: Rethinking the trigger of backdoor attack. arXiv preprint arXiv:2004.04692 (2020) · arXiv
  11. Liao, C., Zhong, H., Squicciarini, A., Zhu, S., Miller, D.: Backdoor embedding in convolutional neural network models via invisible perturbation. arXiv preprint arXiv:1808.10307 (2018) · arXiv
  12. Liu, Y., Li, Y., You, S., Lu, F.: Semantic guided single image reflection removal. arXiv preprint arXiv:1907.11912 (2019) · arXiv
  13. Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019) · arXiv
  14. Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) · arXiv
  15. Yao, Y., Li, H., Zheng, H., Zhao, B.Y.: Latent backdoor attacks on deep neural networks. In: ACM CCS, pp. 2041–2055 (2019) · DOI
  16. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017) · arXiv

Cited by

Sorted by citations

Items 501–536 of 536

Metadata

Authors
Liu, Y., Ma, X., Bailey, J., & Lu, F.
Affiliations
  • State Key Laboratory of Virtual Reality Technology and Systems (CN)
  • Deakin University (AU)
  • The University of Melbourne (AU)
  • Peng Cheng Laboratory (CN)
Corresponding author
Lu, Feng
Venue
Lecture Notes in Computer Science
Published
2020
Pages
182-199
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W3107337211
Cited by
531
References
59
Citations by year
2026: 56 · 2025: 116 · 2024: 128 · 2023: 102 · 2022: 74 · 2021: 47 · 2020: 8
FWCI
22.06
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (61972012)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
Keywords
backdoor attacks, deep neural network, stealthy attacks, backdoor defense, adversarial learning, computer vision

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review