# A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks Source: [A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:20:05.971Z Authors: Warit Sirichotedumrong, Hitoshi Kiya Published: 2020-06-02T01:57:21.000Z ## Abstract We propose a novel image transformation scheme using generative adversarial networks (GANs) for privacy-preserving deep neural networks (DNNs). The proposed scheme enables us not only to apply images without visual information to DNNs, but also to enhance robustness against ciphertext-only attacks (COAs) including DNN-based attacks. In this paper, the proposed transformation scheme is demonstrated to be able to protect visual information on plain images, and the visually-protected images are directly applied to DNNs for privacy-preserving image classification. Since the proposed scheme utilizes GANs, there is no need to manage encryption keys. In an image classification experiment, we evaluate the effectiveness of the proposed scheme in terms of classification accuracy and robustness against COAs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()