Image Anonymization using Deep Convolutional Generative Adversarial Network

Abstract

Advancement in deep learning requires significantly huge amount of data for training purpose, where protection of individual data plays a key role in data privacy and publication. Recent developments in deep learning demonstarte a huge challenge for traditionally used approch for image anonymization, such as model inversion attack, where adversary repeatedly query the model, inorder to reconstrut the original image from the anonymized image. In order to apply more protection on image anonymization, an approach is presented here to convert the input (raw) image into a new synthetic image by applying optimized noise to the latent space representation (LSR) of the original image. The synthetic image is anonymized by adding well designed noise calculated over the gradient during the learning process, where the resultant image is both realistic and immune to model inversion attack. More presicely, we extend the approach proposed by T. Kim and J. Yang, 2019 by using Deep Convolutional Generative Adversarial Network (DCGAN) in order to make the approach more efficient. Our aim is to improve the efficiency of the model by changing the loss function to achieve optimal privacy in less time and computation. Finally, the proposed approach is demonstrated using a benchmark dataset. The experimental study presents that the proposed method can efficiently convert the input image into another synthetic image which is of high quality as well as immune to model inversion attack.

DeepFixCX: Explainable privacy‐preserving image compression for medical image analysis

“…These case-based explanation methods do not perform compression. Another application of a GAN for image anonymization lacks explainability but uses compression to reduce computation (Rao et al, 2021). An approach for classification is to learn linear surrogate models for each class by training them with a differential privacy loss (Harder et al, 2020).…”

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

“…Rao et al [8] presented a study in 2021 that proposed an algorithm to transform the input image matrix into a new output image by applying better noise to the latent space parameters of the original image (LSR). The identity of the synthetic image was concealed by using welldesigned noise computed on the gradient during the learning process, resulting in a realistic image that was resistant to inversion attacks.…”