title: Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on
    Breast Ultrasound Images
summary: Deep neural networks (DNNs) offer significant promise for improving breast
  cancer diagnosis in medical imaging. However, these models are highly
  susceptible to adversarial attacks--small, imperceptible changes that can
  mislead classifiers--raising critical concerns about their reliability and
  security. Traditional attacks rely on fixed-norm perturbations, misaligning
  with human perception. In contrast, diffusion-based attacks require pre-trained
  models, demanding substantial data when these models are unavailable, limiting
  practical use in data-scarce scenarios. In medical imaging, however, this is
  often unfeasible due to the limited availability of datasets. Building on
  recent advancements in learnable prompts, we propose Prompt2Perturb (P2P), a
  novel language-guided attack method capable of generating meaningful attack
  examples driven by text instructions. During the prompt learning phase, our
  approach leverages learnable prompts within the text encoder to create subtle,
  yet impactful, perturbations that remain imperceptible while guiding the model
  towards targeted outcomes. In contrast to current prompt learning-based
  approaches, our P2P stands out by directly updating text embeddings, avoiding
  the need for retraining diffusion models. Further, we leverage the finding that
  optimizing only the early reverse diffusion steps boosts efficiency while
  ensuring that the generated adversarial examples incorporate subtle noise, thus
  preserving ultrasound image quality without introducing noticeable artifacts.
  We show that our method outperforms state-of-the-art attack techniques across
  three breast ultrasound datasets in FID and LPIPS. Moreover, the generated
  images are both more natural in appearance and more effective compared to
  existing adversarial attacks. Our code will be publicly available
  https://github.com/yasamin-med/P2P.
paper_authors._id: 675fdfecdd3e9eeed63d8093
paper_authors.name: Yasamin Medghalchi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:26:08.678Z
paper_authors._id: 650392bb694f6b620fca36e5
paper_authors.avatarUrl: /avatars/35d8f1a0b96835bca81457ff6f361de7.svg
paper_authors.fullname: yasamin medghalchi
paper_authors.type: user
paper_authors.user: yasimed
paper_authors._id: 675fdfecdd3e9eeed63d8094
paper_authors.name: Moein Heidari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T10:26:03.231Z
paper_authors._id: 61ba19bf6122a4fd29049371
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
paper_authors.fullname: Moein Heidari
paper_authors.type: user
paper_authors.user: moein99
paper_authors._id: 675fdfecdd3e9eeed63d8095
paper_authors.name: Clayton Allard
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-18T09:53:02.068Z
paper_authors._id: 6761d1c9ea3649f2ad18b09f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/n7xizu14ObaZHU9Sve-CK.png
paper_authors.fullname: Clayton Allard
paper_authors.type: user
paper_authors.user: claytonallard
paper_authors._id: 675fdfecdd3e9eeed63d8096
paper_authors.name: Leonid Sigal
paper_authors._id: 675fdfecdd3e9eeed63d8097
paper_authors.name: Ilker Hacihaliloglu
paper_title: Prompt2Perturb (P2P): Text-Guided Diffusion-Based Adversarial Attacks on
    Breast Ultrasound Images
paper_summary: Deep neural networks (DNNs) offer significant promise for improving breast
  cancer diagnosis in medical imaging. However, these models are highly
  susceptible to adversarial attacks--small, imperceptible changes that can
  mislead classifiers--raising critical concerns about their reliability and
  security. Traditional attacks rely on fixed-norm perturbations, misaligning
  with human perception. In contrast, diffusion-based attacks require pre-trained
  models, demanding substantial data when these models are unavailable, limiting
  practical use in data-scarce scenarios. In medical imaging, however, this is
  often unfeasible due to the limited availability of datasets. Building on
  recent advancements in learnable prompts, we propose Prompt2Perturb (P2P), a
  novel language-guided attack method capable of generating meaningful attack
  examples driven by text instructions. During the prompt learning phase, our
  approach leverages learnable prompts within the text encoder to create subtle,
  yet impactful, perturbations that remain imperceptible while guiding the model
  towards targeted outcomes. In contrast to current prompt learning-based
  approaches, our P2P stands out by directly updating text embeddings, avoiding
  the need for retraining diffusion models. Further, we leverage the finding that
  optimizing only the early reverse diffusion steps boosts efficiency while
  ensuring that the generated adversarial examples incorporate subtle noise, thus
  preserving ultrasound image quality without introducing noticeable artifacts.
  We show that our method outperforms state-of-the-art attack techniques across
  three breast ultrasound datasets in FID and LPIPS. Moreover, the generated
  images are both more natural in appearance and more effective compared to
  existing adversarial attacks. Our code will be publicly available
  https://github.com/yasamin-med/P2P.
paper_ai_summary: Prompt2Perturb generates subtle, imperceptible adversarial perturbations for breast ultrasound images using text guidance, outperforming existing techniques in both naturalness and effectiveness.
paper_ai_keywords: deep neural networks adversarial attacks diffusion-based attacks pre-trained models learnable prompts prompt learning text encoder text embeddings reverse diffusion FID LPIPS adversarial examples