Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption

• URL: http://arxiv.org/abs/2407.06570v1
• Date: Tue, 9 Jul 2024 06:03:32 GMT
• Title: Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption
• Authors: Umesh Kashyap, Sudev Kumar Padhi, Sk. Subidh Ali,
• Abstract summary: Training state-of-the-art (SOTA) deep learning models requires a large amount of data. Perceptional encryption converts images into an unrecognizable format to protect the sensitive visual information in the training data. This comes at the cost of a significant reduction in the accuracy of the models. Adversarial Visual Information Hiding (AV IH) overcomes this drawback to protect image privacy by attempting to create encrypted images that are unrecognizable to the human eye.
• Score: 1.6385815610837167
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Training state-of-the-art (SOTA) deep learning models requires a large amount of data. The visual information present in the training data can be misused, which creates a huge privacy concern. One of the prominent solutions for this issue is perceptual encryption, which converts images into an unrecognizable format to protect the sensitive visual information in the training data. This comes at the cost of a significant reduction in the accuracy of the models. Adversarial Visual Information Hiding (AV IH) overcomes this drawback to protect image privacy by attempting to create encrypted images that are unrecognizable to the human eye while keeping relevant features for the target model. In this paper, we introduce the Attack GAN (AGAN ) method, a new Generative Adversarial Network (GAN )-based attack that exposes multiple vulnerabilities in the AV IH method. To show the adaptability, the AGAN is extended to traditional perceptual encryption methods of Learnable encryption (LE) and Encryption-then-Compression (EtC). Extensive experiments were conducted on diverse image datasets and target models to validate the efficacy of our AGAN method. The results show that AGAN can successfully break perceptual encryption methods by reconstructing original images from their AV IH encrypted images. AGAN can be used as a benchmark tool to evaluate the robustness of encryption methods for privacy protection such as AV IH.

Related papers

• Unveiling Hidden Visual Information: A Reconstruction Attack Against Adversarial Visual Information Hiding [6.649753747542211]
  A representative image encryption method is the adversarial visual information hiding (AVIH) In the AVIH method, the type-I adversarial example approach creates images that appear completely different but are still recognized by machines as the original ones. We introduce a dual-strategy DR attack against the AVIH encryption method by incorporating generative-adversarial loss and (2) augmented identity loss.
  arXiv  Detail & Related papers  (2024-08-08T06:58:48Z)
• Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples [26.026171363346975]
  Cloud-based image related services such as classification have become crucial. In this study, we propose a novel privacypreserving image classification scheme. encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key.
  arXiv  Detail & Related papers  (2023-10-19T13:01:58Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• 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)
• DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection [64.77548539959501]
  DiffProtect produces more natural-looking encrypted images than state-of-the-art methods. It achieves significantly higher attack success rates, e.g., 24.5% and 25.1% absolute improvements on the CelebA-HQ and FFHQ datasets.
  arXiv  Detail & Related papers  (2023-05-23T02:45:49Z)
• Attribute-Guided Encryption with Facial Texture Masking [64.77548539959501]
  We propose Attribute Guided Encryption with Facial Texture Masking to protect users from unauthorized facial recognition systems. Our proposed method produces more natural-looking encrypted images than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T23:50:43Z)
• Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning [14.505867475659276]
  We propose a novel generative model-based attack on learnable image encryption methods proposed for privacy-preserving deep learning. We use two state-of-the-art generative models: a StyleGAN-based model and latent diffusion-based one. Results show that images reconstructed by the proposed method have perceptual similarities to plain images.
  arXiv  Detail & Related papers  (2023-03-09T05:00:17Z)
• ConfounderGAN: Protecting Image Data Privacy with Causal Confounder [85.6757153033139]
  We propose ConfounderGAN, a generative adversarial network (GAN) that can make personal image data unlearnable to protect the data privacy of its owners. Experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets.
  arXiv  Detail & Related papers  (2022-12-04T08:49:14Z)
• Privacy Safe Representation Learning via Frequency Filtering Encoder [7.792424517008007]
  Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for the task on the server without privacy concerns. We introduce a novel ARL method enhanced through low-pass filtering, limiting the available information amount to be encoded in the frequency domain.
  arXiv  Detail & Related papers  (2022-08-04T06:16:13Z)
• 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)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

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.