Neural image caption generation (NICG) models have received massive attention
from the research community due to their excellent performance in visual
understanding. Existing work focuses on improving NICG model accuracy while
efficiency is less explored. However, many real-world applications require
real-time feedback, which highly relies on the efficiency of NICG models.
Recent research observed that the efficiency of NICG models could vary for
different inputs. This observation brings in a new attack surface of NICG
models, i.e., An adversary might be able to slightly change inputs to cause the
NICG models to consume more computational resources. To further understand such
efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to
evaluate the efficiency robustness of NICG models. Our experimental results
show that NICGSlowDown can generate images with human-unnoticeable
perturbations that will increase the NICG model latency up to 483.86%. We hope
this research could raise the community's concern about the efficiency
robustness of NICG models.

这篇研究提出了一种名为 NICGSlowDown 的新攻击方法，旨在评估神经图像字幕生成模型的效率鲁棒性，并发现输入的变化会导致 NICG 模型的计算资源消耗增加，对 NICG 模型的效率鲁棒性提出了新的挑战。经过实验证明，该攻击方法可以生成几乎无法被人注意到的扰动图像，以增加 NICG 模型的延迟高达 483.86％。