Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix

• URL: http://arxiv.org/abs/2208.02556v1
• Date: Thu, 4 Aug 2022 09:55:31 GMT
• Title: Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix
• Authors: Zheng Qi, AprilPyone MaungMaung, Hitoshi Kiya
• Abstract summary: We propose a privacy-preserving image classification method using encrypted images under the use of the ConvMixer structure. Images with a large size cannot be applied to the conventional method with an adaptation network. We propose a novel method, which allows us not only to apply block-wise scrambled images to ConvMixer for both training and testing without the adaptation network.
• Score: 13.890279045382623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a privacy-preserving image classification method using encrypted images under the use of the ConvMixer structure. Block-wise scrambled images, which are robust enough against various attacks, have been used for privacy-preserving image classification tasks, but the combined use of a classification network and an adaptation network is needed to reduce the influence of image encryption. However, images with a large size cannot be applied to the conventional method with an adaptation network because the adaptation network has so many parameters. Accordingly, we propose a novel method, which allows us not only to apply block-wise scrambled images to ConvMixer for both training and testing without the adaptation network, but also to provide a higher classification accuracy than conventional methods.

Related papers

• Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer [6.476298483207895]
  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. A domain adaptation method is used to efficiently fine-tune ViT with encrypted images.
  arXiv  Detail & Related papers  (2024-01-10T12:46:31Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Human-imperceptible, Machine-recognizable Images [76.01951148048603]
  A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data. This paper proposes an efficient privacy-preserving learning paradigm, where images are encrypted to become human-imperceptible, machine-recognizable'' We show that the proposed paradigm can ensure the encrypted images have become human-imperceptible while preserving machine-recognizable information.
  arXiv  Detail & Related papers  (2023-06-06T13:41:37Z)
• A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models [13.653940190782146]
  A privacy preserving image classification method is proposed under the use of ConvMixer models. The proposed method allows us to use the same classification accuracy as that of ConvMixer models without considering privacy protection.
  arXiv  Detail & Related papers  (2023-01-10T08:21:19Z)
• Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions [78.71745819446176]
  Refign is a generic extension to self-training-based UDA methods which leverages cross-domain correspondences. Refign consists of two steps: (1) aligning the normal-condition image to the corresponding adverse-condition image using an uncertainty-aware dense matching network, and (2) refining the adverse prediction with the normal prediction using an adaptive label correction mechanism. The approach introduces no extra training parameters, minimal computational overhead -- during training only -- and can be used as a drop-in extension to improve any given self-training-based UDA method.
  arXiv  Detail & Related papers  (2022-07-14T11:30:38Z)
• Privacy-Preserving Image Classification Using Vision Transformer [16.679394807198]
  We propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT) ViT utilizes patch embedding and position embedding for image patches, so this architecture is shown to reduce the influence of block-wise image transformation. In an experiment, the proposed method for privacy-preserving image classification is demonstrated to outperform state-of-the-art methods in terms of classification accuracy and robustness against various attacks.
  arXiv  Detail & Related papers  (2022-05-24T12:51:48Z)
• Privacy-Preserving Image Classification Using Isotropic Network [14.505867475659276]
  We propose a privacy-preserving image classification method that uses encrypted images and an isotropic network such as the vision transformer. The proposed method allows us not only to apply images without visual information to deep neural networks (DNNs) for both training and testing but also to maintain a high classification accuracy.
  arXiv  Detail & Related papers  (2022-04-16T03:15:54Z)
• Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps [85.67745220834718]
  We present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes. Our Edge-LBAM method contains dual procedures,including structure-aware mask-updating guided by predict edges. Extensive experiments show that our Edge-LBAM is effective in generating coherent image structures and preventing color discrepancy and blurriness.
  arXiv  Detail & Related papers  (2021-04-25T07:25:16Z)
• Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation [17.134566958534634]
  We propose a transformation network for generating visually-protected images for privacy-preserving DNNs. 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.
  arXiv  Detail & Related papers  (2020-08-07T12:58:45Z)
• Learning to Compose Hypercolumns for Visual Correspondence [57.93635236871264]
  We introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
  arXiv  Detail & Related papers  (2020-07-21T04:03:22Z)
• Cross-modal Deep Face Normals with Deactivable Skip Connections [77.83961745760216]
  We present an approach for estimating surface normals from in-the-wild color images of faces. We propose a method that can leverage all available image and normal data, whether paired or not. We show that our approach can achieve significant improvements, both quantitative and qualitative, with natural face images.
  arXiv  Detail & Related papers  (2020-03-21T16:26:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.