# Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation Source: [Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:29:00.055Z Authors: Hiroki Ito, Yuma Kinoshita, Hitoshi Kiya Published: 2020-08-07T12:58:45.000Z ## Abstract We propose a transformation network for generating visually-protected images for privacy-preserving DNNs. The proposed transformation network is trained by using a plain image dataset so that plain images are transformed into visually protected ones. Conventional perceptual encryption methods have a weak visual-protection performance and some accuracy degradation in image classification. In contrast, the proposed network enables us not only to strongly protect visual information but also to maintain the image classification accuracy that using plain images achieves. In an image classification experiment, the proposed network is demonstrated to strongly protect visual information on plain images without any performance degradation under the use of CIFAR datasets. In addition, it is shown that the visually protected images are robust against a DNN-based attack, called inverse transformation network attack (ITN-Attack) in an experiment. ## 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]()