mlsanityData to 7 Oct 2026

Using a GAN to Generate Adversarial Examples to Facial Image Recognition

arXiv 2111.15213 ↗First posted 30 Nov 2021cs.CVcited by 0

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Abstract (arXiv)

Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system. Such abuse of images is in violation of privacy rights but is difficult to counter. It is well established that adversarial example images can be created for recognition systems which are based on deep neural networks. These adversarial examples can be used to disrupt the utility of the images as reference examples or training data. In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an acceptable success rate in fooling the face recognition. Our results reduce the training time for the GAN by removing the discriminator component. Furthermore, our results show knowledge distillation can be employed to drastically reduce the size of the resulting model without impacting performance indicating that our contribution could run comfortably on a smartphone

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 30 Nov 2021.

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17 of 29 references.

References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  7. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  8. 2016Adversarial examples in the physical worldcited by 1,762
  9. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  10. 2017Adversarial Patchcited by 514
  11. 2018Generating Adversarial Examples with Adversarial Networkscited by 256
  12. 2014Towards Deep Neural Network Architectures Robust to Adversarial Examplescited by 146
5 more
  1. 2018Adversarial Examples: Opportunities and Challengescited by 62
  2. 2017Adversarial Transformation Networks: Learning to Generate Adversarial Examplescited by 60
  3. 2020AI-GAN: Attack-Inspired Generation of Adversarial Examplescited by 22
  4. 2019AdvGAN++ : Harnessing latent layers for adversary generationcited by 20
  5. 2020Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attackscited by 4

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