Diabetic retinopathy (DR) is a leading cause of vision loss in the world and
numerous cutting-edge works have built powerful deep neural networks (DNNs) to
automatically classify the DR cases via the retinal fundus images (RFIs).
However, RFIs are usually affected by the widely existing camera exposure while
the robustness of DNNs to the exposure is rarely explored. In this paper, we
study this problem from the viewpoint of adversarial attack and identify a
totally new task, i.e., adversarial exposure attack generating adversarial
images by tuning image exposure to mislead the DNNs with significantly high
transferability. To this end, we first implement a straightforward method,
i.e., multiplicative-perturbation-based exposure attack, and reveal the big
challenges of this new task. Then, to make the adversarial image naturalness,
we propose the adversarial bracketed exposure fusion that regards the exposure
attack as an element-wise bracketed exposure fusion problem in the
Laplacian-pyramid space. Moreover, to realize high transferability, we further
propose the convolutional bracketed exposure fusion where the element-wise
multiplicative operation is extended to the convolution. We validate our method
on the real public DR dataset with the advanced DNNs, e.g., ResNet50,
MobileNet, and EfficientNet, showing our method can achieve high image quality
and success rate of the transfer attack. Our method reveals the potential
threats to the DNN-based DR automated diagnosis and can definitely benefit the
development of exposure-robust automated DR diagnosis method in the future.

通过对对抗攻击的视角进行研究，我们提出了一种全新的任务，即对抗曝光攻击，通过调整图像曝光来生成对抗图像以误导神经网络，在 Laplacian-pyramid 空间中将暴露攻击视为逐元素的括号曝光融合问题，同时通过扩展元素逐一乘法运算来实现具有高可转移性的卷积括号曝光融合，最终我们在真实公共 DR 数据集上验证了我们的方法，证明了我们的方法具有高质量图像和转移攻击的成功率，并将 DR 自动诊断方法的发展有所裨益。