Resource efficiency plays an important role for machine learning nowadays.
The energy and decision latency are two critical aspects to ensure a
sustainable and practical application. Unfortunately, the energy consumption
and decision latency are not robust against adversaries. Researchers have
recently demonstrated that attackers can compute and submit so-called sponge
examples at inference time to increase the energy consumption and decision
latency of neural networks. In computer vision, the proposed strategy crafts
inputs with less activation sparsity which could otherwise be used to
accelerate the computation. In this paper, we analyze the mechanism how these
energy-latency attacks reduce activation sparsity. In particular, we find that
input uniformity is a key enabler. A uniform image, that is, an image with
mostly flat, uniformly colored surfaces, triggers more activations due to a
specific interplay of convolution, batch normalization, and ReLU activation.
Based on these insights, we propose two new simple, yet effective strategies
for crafting sponge examples: sampling images from a probability distribution
and identifying dense, yet inconspicuous inputs in natural datasets. We
empirically examine our findings in a comprehensive evaluation with multiple
image classification models and show that our attack achieves the same sparsity
effect as prior sponge-example methods, but at a fraction of computation
effort. We also show that our sponge examples transfer between different neural
networks. Finally, we discuss applications of our findings for the good by
improving efficiency by increasing sparsity.

机器学习中的资源效率对于当今十分重要，本文分析了敌对攻击如何通过减少激活稀疏度的方式来增加能耗和决策延迟，并提出了两种新的简单而有效的策略来生成海绵样本，通过实证评估表明这些策略能够在少量计算资源的情况下取得与之前方法相当的激活稀疏效果，并且能够在不同的神经网络之间进行迁移。