发表机构
Indian Institute of Technology Guwahati; Nanyang Technological University; Prince of Songkla University（印度古瓦哈蒂印度理工学院; 南洋理工大学; 宋卡王子大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对攻击者破坏ViT令牌剪枝效率提升的定向攻击，提出模型无关的预处理防御流水线MOAT，可将对抗性攻击下ViT的GFLOPs下降控制在3.4%以内。
AI 中文摘要
为将视觉Transformer（ViTs）应用于资源受限环境，令牌剪枝被广泛用于在不影响准确率的前提下降低计算成本。然而，攻击者已开发出针对该令牌剪枝技术的定向攻击，以破坏提升ViTs效率的尝试。本文提出MOAT，一种模型无关的预处理防御流水线，通过应用输入变换的组合来保护高效ViT实现免受对抗性效率攻击。MOAT直接作用于输入，无需修改模型架构或令牌剪枝机制。实验结果表明，在所有评估的ViT模型中，MOAT可将对抗性攻击下的GFLOPs下降限制在原始未受攻击模型的3.4%以内。
英文摘要
To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacting accuracy. However, adversaries have developed targeted attacks against said token pruning techniques to undermine such attempts to make ViTs efficient. In this paper, we propose MOAT, a model-agnostic pre-processing defense pipeline that applies a combination of input transformations to protect efficient ViT implementations against adversarial efficiency attacks. MOAT operates directly on the input without requiring modifications to the model architecture or token pruning mechanism. Experimental results demonstrate that, across all evaluated ViT models, MOAT limits GFLOPs degradation under adversarial attacks to within 3.4% of the original unattacked model.
CommentsThis paper has been accepted for publication at IEEE ISVLSI 2026
