提出MOAT防御方法，保护视觉Transformer在资源受限环境下的效率
MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs
- 通过输入随机变换构建模型无关的防御管道
- 对抗攻击下计算量下降不超过原始模型的3.4%
- 无需修改模型或剪枝机制，适合实际部署
为在资源受限环境下应用视觉Transformer（ViTs），通常采用标记剪枝以降低计算开销而不影响准确率。然而，攻击者已开发针对此类剪枝技术的定向攻击，破坏高效ViT的实现。本文提出MOAT，一种模型无关的预处理防御方案，通过组合输入变换来保护高效ViT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剪枝机制。实验结果表明，在所有评估的ViT模型中，MOAT将对抗攻击下的GFLOPs退化控制在原始未受攻击模型的3.4%以内。
原文摘要 · Abstract (English)
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.
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