Adversarially Learned Representations for Information Obfuscation and Inference
@inproceedings{Bertrn2019AdversariallyLR, title={Adversarially Learned Representations for Information Obfuscation and Inference}, author={Mart{\'i}n Bertr{\'a}n and N. Mart{\'i}nez and A. Papadaki and Q. Qiu and M. Rodrigues and G. Reeves and G. Sapiro}, booktitle={ICML}, year={2019} }
Data collection and sharing are pervasive aspects of modern society. This process can either be voluntary, as in the case of a person taking a facial image to unlock his/her phone, or incidental, such as traffic cameras collecting videos on pedestrians. An undesirable side effect of these processes is that shared data can carry information about attributes that users might consider as sensitive, even when such information is of limited use for the task. It is therefore desirable for both data… CONTINUE READING
Supplemental Presentations
Figures, Tables, and Topics from this paper
14 Citations
Adversarial representation learning for synthetic replacement of private attributes
- Computer Science, Mathematics
- ArXiv
- 2020
- 2
- PDF
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
- Computer Science, Mathematics
- NeurIPS
- 2020
- PDF
Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset
- Computer Science
- ArXiv
- 2019
- 9
Towards Generalized and Distributed Privacy-Preserving Representation Learning
- Computer Science, Mathematics
- ArXiv
- 2020
- 1
- Highly Influenced
- PDF
Imparting Fairness to Pre-Trained Biased Representations
- Computer Science
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- 2020
- 1
- PDF
Censored and Fair Universal Representations using Generative Adversarial Models
- Computer Science, Mathematics
- 2019
- 9
- PDF
On the Global Optima of Kernelized Adversarial Representation Learning
- Computer Science, Mathematics
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2019
- 8
- PDF
Preserving Privacy in Image-based Emotion Recognition through User Anonymization
- Computer Science
- ICMI
- 2020
References
SHOWING 1-10 OF 40 REFERENCES
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective
- Computer Science, Mathematics
- 2017 IEEE International Conference on Computer Vision (ICCV)
- 2017
- 77
- PDF
Privacy-preserving deep learning
- Computer Science
- 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
- 2015
- 829
- PDF
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
- Computer Science, Mathematics
- NIPS
- 2017
- 262
- PDF
Protecting Visual Secrets Using Adversarial Nets
- Computer Science
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- 2017
- 38
- PDF
Learning Adversarially Fair and Transferable Representations
- Computer Science, Mathematics
- ICML
- 2018
- 193
- PDF
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
- Computer Science
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- 2018
- 31
- PDF
Adversarial Discriminative Domain Adaptation
- Computer Science
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2017
- 1,743
- PDF