arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1704.01547stat.MLcs.LGq-bio.NC

Comment on "Biologically inspired protection of deep networks from adversarial attacks"

University of Tübingen(图宾根大学);Bernstein Center for Computational Neuroscience(伯恩斯坦计算神经科学中心);Max Planck Institute for Biological Cybernetics(马克斯·普朗克生物控制论研究所)

Wieland Brendel, Matthias Bethge

英文摘要:

A recent paper suggests that Deep Neural Networks can be protected from gradient-based adversarial perturbations by driving the network activations into a highly saturated regime. Here we analyse such saturated networks and show that the attacks fail due to numerical limitations in the gradient computations. A simple stabilisation of the gradient estimates enables successful and efficient attacks. Thus, it has yet to be shown that the robustness observed in highly saturated networks is not simply due to numerical limitations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑