# Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer Source: [Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:19:18.470Z Authors: Teru Nagamori, Sayaka Shiota, Hitoshi Kiya Published: 2024-01-10T12:46:31.000Z ## Abstract We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT). The method allows us not only to train models and test with visually protected images but to also avoid the performance degradation caused from the use of encrypted images, whereas conventional methods cannot avoid the influence of image encryption. A domain adaptation method is used to efficiently fine-tune ViT with encrypted images. In experiments, the method is demonstrated to outperform conventional methods in an image classification task on the CIFAR-10 and ImageNet datasets in terms of classification accuracy. ## 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]()