mlsanityData to 7 Oct 2026

Privacy protection based on mask template

arXiv 2202.06250 ↗First posted 13 Feb 2022cs.CR cs.AI cs.CVcited by 0

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Abstract (arXiv)

Powerful recognition algorithms are widely used in the Internet or important medical systems, which poses a serious threat to personal privacy. Although the law provides for diversity protection, e.g. The General Data Protection Regulation (GDPR) in Europe and Articles 1032 to 1039 of the civil code in China. However, as an important privacy disclosure event, biometric data is often hidden, which is difficult for the owner to detect and trace to the source. Human biometrics generally exist in images. In order to avoid the disclosure of personal privacy, we should prevent unauthorized recognition algorithms from acquiring the real features of the original image.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 13 Feb 2022.

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23 of 39 references.

References · 23

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  3. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
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  5. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  6. 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 550
  7. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  8. 2017Machine Learning Models that Remember Too Muchcited by 165
  9. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 161
  10. 2017Neural Trojanscited by 151
  11. 2017Adversarial Frontier Stitching for Remote Neural Network Watermarkingcited by 110
  12. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 78
11 more
  1. 2019Data Poisoning against Differentially-Private Learners: Attacks and Defensescited by 75
  2. 2018Poisoning Attacks to Graph-Based Recommender Systemscited by 52
  3. 2017Generative Poisoning Attack Method Against Neural Networkscited by 49
  4. 2018Digital Watermarking for Deep Neural Networkscited by 37
  5. 2015FireCaffe: near-linear acceleration of deep neural network training on compute clusterscited by 25
  6. 2018DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networkscited by 23
  7. 2020Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networkscited by 16
  8. 2020Fawkes: Protecting Privacy against Unauthorized Deep Learning Modelscited by 11
  9. 2018Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniquescited by 10
  10. 2018PoTrojan: powerful neural-level trojan designs in deep learning modelscited by 9
  11. 2018Are You Tampering With My Data?cited by 6

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