Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack

Ähnliche Einträge

• Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
  von: Zheng, Weijie, et al.
  Veröffentlicht: (2024)
• A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models
  von: Chen, Wutao, et al.
  Veröffentlicht: (2026)
• HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
  von: Liu, Han, et al.
  Veröffentlicht: (2026)
• Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models
  von: Kong, Dehong, et al.
  Veröffentlicht: (2024)
• VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
  von: Yin, Ziyi, et al.
  Veröffentlicht: (2024)