Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation

• URL: http://arxiv.org/abs/2411.16207v1
• Date: Mon, 25 Nov 2024 09:14:53 GMT
• Title: Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation
• Authors: XiaoKai Cao, WenJin Mo, ChangDong Wang, JianHuang Lai, Qiong Huang,
• Abstract summary: We establish a novel framework for measuring image information content to evaluate the variation in information content during image transformations. We also propose a novel image encryption algorithm called Random Vortex Transformation.
• Score: 51.475827684468875
• License:
• Abstract: Vision is one of the essential sources through which humans acquire information. In this paper, we establish a novel framework for measuring image information content to evaluate the variation in information content during image transformations. Within this framework, we design a nonlinear function to calculate the neighboring information content of pixels at different distances, and then use this information to measure the overall information content of the image. Hence, we define a function to represent the variation in information content during image transformations. Additionally, we utilize this framework to prove the conclusion that swapping the positions of any two pixels reduces the image's information content. Furthermore, based on the aforementioned framework, we propose a novel image encryption algorithm called Random Vortex Transformation. This algorithm encrypts the image using random functions while preserving the neighboring information of the pixels. The encrypted images are difficult for the human eye to distinguish, yet they allow for direct training of the encrypted images using machine learning methods. Experimental verification demonstrates that training on the encrypted dataset using ResNet and Vision Transformers only results in a decrease in accuracy ranging from 0.3\% to 6.5\% compared to the original data, while ensuring the security of the data. Furthermore, there is a positive correlation between the rate of information loss in the images and the rate of accuracy loss, further supporting the validity of the proposed image information content measurement framework.

Related papers

• Enhancing Historical Image Retrieval with Compositional Cues [3.2276097734075426]
  We introduce a crucial factor from computational aesthetics, namely image composition, into this topic. By explicitly integrating composition-related information extracted by CNN into the designed retrieval model, our method considers both the image's composition rules and semantic information.
  arXiv  Detail & Related papers  (2024-03-21T10:51:19Z)
• Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings [22.338328674283062]
  We introduce an innovative image transformation technique that renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. The proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. We show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy.
  arXiv  Detail & Related papers  (2024-01-26T18:20:53Z)
• On Mask-based Image Set Desensitization with Recognition Support [46.51027529020668]
  We propose a mask-based image desensitization approach while supporting recognition. We exploit an interpretation algorithm to maintain critical information for the recognition task. In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images.
  arXiv  Detail & Related papers  (2023-12-14T14:26:42Z)
• EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithm [0.7673339435080445]
  The aim of steganographic algorithms is to identify the appropriate pixel positions in the host or cover image, where bits of sensitive information can be concealed for data encryption. Work is being done to improve the capacity to integrate sensitive information and to maintain the visual appearance of the steganographic image. We use the Shuffled Frog Leaping Algorithm (SFLA) to determine the order of pixels by which sensitive information can be placed in the cover image.
  arXiv  Detail & Related papers  (2023-12-13T17:34:08Z)
• Perceptual Image Compression with Cooperative Cross-Modal Side Information [53.356714177243745]
  We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
  arXiv  Detail & Related papers  (2023-11-23T08:31:11Z)
• 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)
• Memory-Driven Text-to-Image Generation [126.58244124144827]
  We introduce a memory-driven semi-parametric approach to text-to-image generation. Non-parametric component is a memory bank of image features constructed from a training set of images. parametric component is a generative adversarial network.
  arXiv  Detail & Related papers  (2022-08-15T06:32:57Z)
• Data Augmentation for Object Detection via Differentiable Neural Rendering [71.00447761415388]
  It is challenging to train a robust object detector when annotated data is scarce. Existing approaches to tackle this problem include semi-supervised learning that interpolates labeled data from unlabeled data. We introduce an offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
  arXiv  Detail & Related papers  (2021-03-04T06:31:06Z)
• Learning Transformation-Aware Embeddings for Image Forensics [15.484408315588569]
  Image Provenance Analysis aims at discovering relationships among different manipulated image versions that share content. One of the main sub-problems for provenance analysis that has not yet been addressed directly is the edit ordering of images that share full content or are near-duplicates. This paper introduces a novel deep learning-based approach to provide a plausible ordering to images that have been generated from a single image through transformations.
  arXiv  Detail & Related papers  (2020-01-13T22:01:24Z)
• Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
  We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image. We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
  arXiv  Detail & Related papers  (2020-01-12T05:26:47Z)

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.