# Learnable Image Encryption

arXiv: https://arxiv.org/abs/1804.00490 · HTML: https://mlsanity.com/paper/1804.00490/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 30 of 33)

- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [Privacy-Preserving Image Classification Using Isotropic Network](https://mlsanity.com/paper/2204.07707/) (2022)
- [A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [Image and Model Transformation with Secret Key for Vision Transformer](https://mlsanity.com/paper/2207.05366/) (2022)
- [NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training](https://mlsanity.com/paper/2106.02484/) (2021)
- [Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key](https://mlsanity.com/paper/2104.04241/) (2021)
- [Privacy-Preserving Image Classification Using Vision Transformer](https://mlsanity.com/paper/2205.12041/) (2022)
- [Hiding Visual Information via Obfuscating Adversarial Perturbations](https://mlsanity.com/paper/2209.15304/) (2022)
- [A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access](https://mlsanity.com/paper/2109.00224/) (2021)
- [Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models](https://mlsanity.com/paper/2109.01332/) (2021)
- [On the Transferability of Adversarial Examples between Encrypted Models](https://mlsanity.com/paper/2209.02997/) (2022)
- [Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2303.05036/) (2023)
- [Privacy-Preserving Remote Heart Rate Estimation from Facial Videos](https://mlsanity.com/paper/2306.01141/) (2023)
- [Syfer: Neural Obfuscation for Private Data Release](https://mlsanity.com/paper/2201.12406/) (2022)
- [Access Control of Semantic Segmentation Models Using Encrypted Feature Maps](https://mlsanity.com/paper/2206.05422/) (2022)
- [Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer](https://mlsanity.com/paper/2401.05126/) (2024)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks](https://mlsanity.com/paper/2301.04875/) (2023)
- [Transfer Learning-Based Model Protection With Secret Key](https://mlsanity.com/paper/2103.03525/) (2021)
- [StyleGAN Encoder-Based Attack for Block Scrambled Face Images](https://mlsanity.com/paper/2209.07953/) (2022)
- [Access Control with Encrypted Feature Maps for Object Detection Models](https://mlsanity.com/paper/2209.14831/) (2022)
- [Human-imperceptible, Machine-recognizable Images](https://mlsanity.com/paper/2306.03679/) (2023)
- [Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images](https://mlsanity.com/paper/2309.02556/) (2023)
- [Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning](https://mlsanity.com/paper/2404.05828/) (2024)
- [A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning](https://mlsanity.com/paper/2105.08876/) (2021)
- [A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models](https://mlsanity.com/paper/2203.16780/) (2022)
- [Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix](https://mlsanity.com/paper/2208.02556/) (2022)
- [On the Adversarial Transferability of ConvMixer Models](https://mlsanity.com/paper/2209.08724/) (2022)
- [A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models](https://mlsanity.com/paper/2301.03843/) (2023)
- [Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack](https://mlsanity.com/paper/2403.10558/) (2024)

## Abstract (arXiv)

The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues. In this paper, a simple learnable image encryption algorithm is proposed. Then, the proposed algorithm is validated with cifar dataset.