# Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption

arXiv: https://arxiv.org/abs/2407.06570 · HTML: https://mlsanity.com/paper/2407.06570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [SHE: A Fast and Accurate Deep Neural Network for Encrypted Data](https://mlsanity.com/paper/1906.00148/) (2019)
- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Learnable Image Encryption](https://mlsanity.com/paper/1804.00490/) (2018)
- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2006.01342/) (2020)
- [Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domain](https://mlsanity.com/paper/1905.01827/) (2019)
- [Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2004.13263/) (2020)
- [DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things](https://mlsanity.com/paper/2004.05523/) (2020)
- [Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation](https://mlsanity.com/paper/2008.03143/) (2020)
- [Hiding Visual Information via Obfuscating Adversarial Perturbations](https://mlsanity.com/paper/2209.15304/) (2022)
- [Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2303.05036/) (2023)

## Abstract (arXiv)

Training state-of-the-art (SOTA) deep learning models requires a large amount of data. The visual information present in the training data can be misused, which creates a huge privacy concern. One of the prominent solutions for this issue is perceptual encryption, which converts images into an unrecognizable format to protect the sensitive visual information in the training data. This comes at the cost of a significant reduction in the accuracy of the models. Adversarial Visual Information Hiding (AV IH) overcomes this drawback to protect image privacy by attempting to create encrypted images that are unrecognizable to the human eye while keeping relevant features for the target model. In this paper, we introduce the Attack GAN (AGAN ) method, a new Generative Adversarial Network (GAN )-based attack that exposes multiple vulnerabilities in the AV IH method. To show the adaptability, the AGAN is extended to traditional perceptual encryption methods of Learnable encryption (LE) and Encryption-then-Compression (EtC). Extensive experiments were conducted on diverse image datasets and target models to validate the efficacy of our AGAN method. The results show that AGAN can successfully break perceptual encryption methods by reconstructing original images from their AV IH encrypted images. AGAN can be used as a benchmark tool to evaluate the robustness of encryption methods for privacy protection such as AV IH.