# Visual Privacy Protection via Mapping Distortion

arXiv: https://arxiv.org/abs/1911.01769 · HTML: https://mlsanity.com/paper/1911.01769/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection](https://mlsanity.com/paper/2210.00875/) (2022)
- [Black-box Dataset Ownership Verification via Backdoor Watermarking](https://mlsanity.com/paper/2209.06015/) (2022)
- [Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand](https://mlsanity.com/paper/2310.14942/) (2023)
- [CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking](https://mlsanity.com/paper/2503.05794/) (2025)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [CertDW: Towards Certified Dataset Ownership Verification via Conformal Calibration](https://mlsanity.com/paper/2506.13160/) (2025)

## Abstract (arXiv)

Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios. In this paper, we propose the mapping distortion based protection (MDP) and its augmentation-based extension (AugMDP) to protect the data privacy by modifying the original dataset. In the modified dataset generated by MDP, the image and its label are not consistent (e.g., a cat-like image is labeled as the dog), whereas the DNNs trained on it can still achieve good performance on benign testing set. As such, this method can protect privacy when the dataset is leaked. Extensive experiments are conducted, which verify the effectiveness and feasibility of our method. The code for reproducing main results is available at urlhttps://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion.