# Block-wise Scrambled Image Recognition Using Adaptation Network

arXiv: https://arxiv.org/abs/2001.07761 · HTML: https://mlsanity.com/paper/2001.07761/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 27 of 27)

- [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)
- [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)
- [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)
- [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)
- [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)
- [ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification](https://mlsanity.com/paper/2411.05901/) (2024)
- [AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach](https://mlsanity.com/paper/2501.15363/) (2025)
- [A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models](https://mlsanity.com/paper/2203.16780/) (2022)
- [An Encryption Method of ConvMixer Models without Performance Degradation](https://mlsanity.com/paper/2207.11939/) (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)
- [Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack](https://mlsanity.com/paper/2403.10558/) (2024)
- [Privacy-Preserving Vision Transformer Using Images Encrypted with Restricted Random Permutation Matrices](https://mlsanity.com/paper/2408.08529/) (2024)
- [Privacy-Preserving Semantic Segmentation without Key Management](https://mlsanity.com/paper/2604.16523/) (2026)
- [Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations](https://mlsanity.com/paper/2608.27066/) (2026)

## Abstract (arXiv)

In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines. Hence, both the perceptual information hiding and the corresponding object recognition methods should be developed. Block-wise image scrambling is introduced to hide perceptual information from a third party. In addition, an adaptation network is proposed to recognize those scrambled images. Experimental comparisons conducted using CIFAR datasets demonstrated that the proposed adaptation network performed well in incorporating simple perceptual information hiding into DNN-based image classification.