literature Tree
论文解析树
  Abstract
    Task
      evaluate the efficiency robustness of NICG models
    Technical Challenge
      efficiency is less explored，另一个视角
    key insight/motivation
      adversary change cause the NICG model tp consume more computational resources
    technical contribution
      a new attack approach that evaluate the efficiency robustness of NICG models
  Introduction
    Task And application
    Technical challenge for previous problem
      previous method
        都是accuracy based
      failure cases
        不能影响effectiveness
    our pipeline that fix
      key innovation/insight/contribution
        apply the minimal perturbation on the benign inputs that could minimize the likelihood of End Of Sentence (EOS) token and delay the appearance of EOS accordingly
      contribution 1
        a new vulnerability
      contribution2
        a new methodology to evaluate the efficiency robustness of NICG models
      contribution3
        evaluate NICGSlowDown
  Method
      在满足约束的情况下，decoder调用次数最多
      使用latent variable 保证
      loss function
        delay the apprearence of EOS
        idea
          NCE noise contrastive estimation
        EOS prob
            不用MLE是因为计算复杂度太高了
            证明收敛至MLE
            slowdown.py&captionAPI.py
            slowdown.py
        Pertubation penalty
          compute_per函数
        technical advantage
  Experiments
    2 datasets
      coco
      flick8k
    4 subjects
    metrics
  Limitation
    白盒模型，需要知道模型的具体细节
    讨论
      模型的最大token数越大，越容易受latency attack影响
      但是用户的要求场景变化多，不能简单缩短 max token