mlsanityData to 14 Oct 2026

Learnable Image Encryption

arXiv 1804.00490 ↗First posted 19 Mar 2018cs.CV cs.LGcited by 33

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 8; 2022: 13; 2023: 6; 2024: 5; 2025: 0; 2026: 1.

33 papers.

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  1. 2022An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applicationscited by 29
  2. 2022Privacy-Preserving Image Classification Using Isotropic Networkcited by 17
  3. 2021A Protection Method of Trained CNN Model with Secret Key from Unauthorized Accesscited by 15
  4. 2022Image and Model Transformation with Secret Key for Vision Transformercited by 12
  5. 2021NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Trainingcited by 11
  6. 2021Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Keycited by 10
  7. 2022Privacy-Preserving Image Classification Using Vision Transformercited by 9
  8. 2022Hiding Visual Information via Obfuscating Adversarial Perturbationscited by 7
  9. 2021A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Accesscited by 4
  10. 2021Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Modelscited by 4
  11. 2022On the Transferability of Adversarial Examples between Encrypted Modelscited by 4
  12. 2023Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learningcited by 4
21 more
  1. 2023Privacy-Preserving Remote Heart Rate Estimation from Facial Videoscited by 4
  2. 2022Syfer: Neural Obfuscation for Private Data Releasecited by 3
  3. 2022Access Control of Semantic Segmentation Models Using Encrypted Feature Mapscited by 3
  4. 2024Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformercited by 3
  5. 2021Multitask Identity-Aware Image Steganography via Minimax Optimizationcited by 2
  6. 2023Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networkscited by 2
  7. 2021Transfer Learning-Based Model Protection With Secret Keycited by 1
  8. 2022StyleGAN Encoder-Based Attack for Block Scrambled Face Imagescited by 1
  9. 2022Access Control with Encrypted Feature Maps for Object Detection Modelscited by 1
  10. 2023Human-imperceptible, Machine-recognizable Imagescited by 1
  11. 2023Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Imagescited by 1
  12. 2024Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learningcited by 1
  13. 2021A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learningcited by 0
  14. 2022A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Modelscited by 0
  15. 2022Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrixcited by 0
  16. 2022On the Adversarial Transferability of ConvMixer Modelscited by 0
  17. 2023A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Modelscited by 0
  18. 2024Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attackcited by 0
  19. 2024Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryptioncited by 0
  20. 2024NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representationcited by 0
  21. 2026Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformationscited by 0