Exploring Adversarial Watermarking in Transformer-Based Models: Transferability and Robustness Against Defense Mechanism for Medical Images

• URL: http://arxiv.org/abs/2506.06389v1
• Date: Thu, 05 Jun 2025 15:03:38 GMT
• Title: Exploring Adversarial Watermarking in Transformer-Based Models: Transferability and Robustness Against Defense Mechanism for Medical Images
• Authors: Rifat Sadik, Tanvir Rahman, Arpan Bhattacharjee, Bikash Chandra Halder, Ismail Hossain,
• Abstract summary: Vision Transformers (ViTs) is a transformer-based models that have shown success in computer vision.<n>This paper investigates the susceptibility of ViTs for medical images to adversarial watermarking.
• Score: 0.6817102408452475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense popularity and success in computer vision (CV) based task like skin image recognition, generation and video analysis. But with the emergence of transformer based models, CV tasks are now are nowadays carrying out using these models. Vision Transformers (ViTs) is such a transformer-based models that have shown success in computer vision. It uses self-attention mechanisms to achieve state-of-the-art performance across various tasks. However, their reliance on global attention mechanisms makes them susceptible to adversarial perturbations. This paper aims to investigate the susceptibility of ViTs for medical images to adversarial watermarking-a method that adds so-called imperceptible perturbations in order to fool models. By generating adversarial watermarks through Projected Gradient Descent (PGD), we examine the transferability of such attacks to CNNs and analyze the performance defense mechanism -- adversarial training. Results indicate that while performance is not compromised for clean images, ViTs certainly become much more vulnerable to adversarial attacks: an accuracy drop of as low as 27.6%. Nevertheless, adversarial training raises it up to 90.0%.

Related papers

• Mechanistic Understandings of Representation Vulnerabilities and Engineering Robust Vision Transformers [1.1187085721899017]
  We study the sources of known representation vulnerabilities of vision transformers (ViT), where perceptually identical images can have very different representations.<n>We develop NeuroShield-ViT, a novel defense mechanism that strategically neutralizes vulnerable neurons in earlier layers to prevent the cascade of adversarial effects.<n>Our results shed new light on how adversarial effects propagate through ViT layers, while providing a promising approach to enhance the robustness of vision transformers against adversarial attacks.
  arXiv  Detail & Related papers  (2025-02-07T05:58:16Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks [4.295229451607423]
  Vision Transformer (ViT) is becoming widely popular in automating accurate disease diagnosis in medical imaging. ViTs remain vulnerable to adversarial attacks that may thwart the diagnosis process by leading it to intentional misclassification of critical disease. We propose a novel image classification pipeline, namely, S-E Pipeline, that performs multiple pre-processing steps.
  arXiv  Detail & Related papers  (2024-07-23T17:20:40Z)
• Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation [17.46080957271494]
  The scarcity of publicly available medical images has led contemporary algorithms to depend on pretrained models grounded on a large set of natural images. A significant em domain discrepancy exists between natural and medical images, which causes AI models to exhibit heightened em vulnerability to adversarial attacks. This paper proposes a em domain assimilation approach that introduces texture and color adaptation into transfer learning, followed by a texture preservation component to suppress undesired distortion.
  arXiv  Detail & Related papers  (2024-02-25T06:39:15Z)
• MedViT: A Robust Vision Transformer for Generalized Medical Image Classification [4.471084427623774]
  We propose a robust yet efficient CNN-Transformer hybrid model which is equipped with the locality of CNNs and the global connectivity of vision Transformers. Our proposed hybrid model demonstrates its high robustness and generalization ability compared to the state-of-the-art studies on a large-scale collection of standardized MedMNIST-2D datasets.
  arXiv  Detail & Related papers  (2023-02-19T02:55:45Z)
• Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification [4.843654097048771]
  Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging. Recent works have shown that ViTs are also susceptible to such attacks and suffer significant performance degradation under attack. We propose a novel self-ensembling method to enhance the robustness of ViT in the presence of adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-04T19:02:24Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Understanding The Robustness in Vision Transformers [140.1090560977082]
  Self-attention may promote robustness through improved mid-level representations. We propose a family of fully attentional networks (FANs) that strengthen this capability. Our model achieves a state of-the-art 87.1% accuracy and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters.
  arXiv  Detail & Related papers  (2022-04-26T17:16:32Z)
• Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications [72.52228843498193]
  In training deep learning models, a compromise often must be made between performance and trust. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients.
  arXiv  Detail & Related papers  (2021-12-14T21:16:52Z)
• Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks [104.8737334237993]
  We present comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For five popular image-to-image tasks, 16 deep models are analyzed from various standpoints. We show that unlike in image classification tasks, the performance degradation on image-to-image tasks can largely differ depending on various factors.
  arXiv  Detail & Related papers  (2021-04-30T14:20:33Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.