# DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones

arXiv: https://arxiv.org/abs/1909.04126 · HTML: https://mlsanity.com/paper/1909.04126/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 15 of 15)

- [Privacy-preserving Adversarial Facial Features](https://mlsanity.com/paper/2305.05391/) (2023)
- [Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaboration](https://mlsanity.com/paper/2503.12896/) (2025)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [DRAG: Data Reconstruction Attack using Guided Diffusion](https://mlsanity.com/paper/2509.11724/) (2025)
- [Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models](https://mlsanity.com/paper/2401.07205/) (2024)
- [Privacy Safe Representation Learning via Frequency Filtering Encoder](https://mlsanity.com/paper/2208.02482/) (2022)
- [Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning](https://mlsanity.com/paper/2209.12046/) (2022)
- [PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks](https://mlsanity.com/paper/2412.14499/) (2024)
- [Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers](https://mlsanity.com/paper/2112.15111/) (2021)
- [MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning](https://mlsanity.com/paper/2305.07815/) (2023)
- [Facial Data Minimization: Shallow Model as Your Privacy Filter](https://mlsanity.com/paper/2310.15590/) (2023)
- [Perturbation Effects on Robustness and Individual Fairness](https://mlsanity.com/paper/2404.01356/) (2024)
- [PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks](https://mlsanity.com/paper/2408.05092/) (2024)
- [CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation](https://mlsanity.com/paper/2512.12086/) (2025)
- [Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encoding](https://mlsanity.com/paper/2608.23382/) (2026)

## Abstract (arXiv)

Deep learning has been widely applied in many computer vision applications, with remarkable success. However, running deep learning models on mobile devices is generally challenging due to the limitation of computing resources. A popular alternative is to use cloud services to run deep learning models to process raw data. This, however, imposes privacy risks. Some prior arts proposed sending the features extracted from raw data to the cloud. Unfortunately, these extracted features can still be exploited by attackers to recover raw images and to infer embedded private attributes. In this paper, we propose an adversarial training framework, DeepObfuscator, which prevents the usage of the features for reconstruction of the raw images and inference of private attributes. This is done while retaining useful information for the intended cloud service. DeepObfuscator includes a learnable obfuscator that is designed to hide privacy-related sensitive information from the features by performing our proposed adversarial training algorithm. The proposed algorithm is designed by simulating the game between an attacker who makes efforts to reconstruct raw image and infer private attributes from the extracted features and a defender who aims to protect user privacy. By deploying the trained obfuscator on the smartphone, features can be locally extracted and then sent to the cloud. Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0.9458 to 0.3175 in terms of multi-scale structural similarity. The person in the reconstructed image, hence, becomes hardly to be re-identified. The classification accuracy of the inferred private attributes that can be achieved by the attacker is significantly reduced to a random-guessing level.