Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement
This addresses robustness issues in semantic segmentation models for computer vision applications, but it is incremental as it builds on existing attention-based methods.
The paper tackles the vulnerability of attention mechanisms in semantic segmentation to patch-based adversarial attacks by proposing a Robust Attention Mechanism (RAM) with Max Attention Suppression and Random Attention Dropout modules, which notably relieves this vulnerability as demonstrated in extensive experiments.
The attention mechanism has been proven effective on various visual tasks in recent years. In the semantic segmentation task, the attention mechanism is applied in various methods, including the case of both Convolution Neural Networks (CNN) and Vision Transformer (ViT) as backbones. However, we observe that the attention mechanism is vulnerable to patch-based adversarial attacks. Through the analysis of the effective receptive field, we attribute it to the fact that the wide receptive field brought by global attention may lead to the spread of the adversarial patch. To address this issue, in this paper, we propose a Robust Attention Mechanism (RAM) to improve the robustness of the semantic segmentation model, which can notably relieve the vulnerability against patch-based attacks. Compared to the vallina attention mechanism, RAM introduces two novel modules called Max Attention Suppression and Random Attention Dropout, both of which aim to refine the attention matrix and limit the influence of a single adversarial patch on the semantic segmentation results of other positions. Extensive experiments demonstrate the effectiveness of our RAM to improve the robustness of semantic segmentation models against various patch-based attack methods under different attack settings.