Abstract
Recently salient object detection has witnessed remarkable improvement owing to the deep convolutional neural networks which can harvest powerful features for images. In particular, state-of-the-art salient object detection methods enjoy high accuracy and efficiency from fully convolutional network (FCN) based frameworks which are trained from end to end and predict pixel-wise labels. However, such framework suffers from adversarial attacks which confuse neural networks via adding quasi-imperceptible noises to input images without changing the ground truth annotated by human subjects. To our knowledge, this paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods. Furthermore, this paper proposes a novel end-to-end trainable framework to enhance the robustness for arbitrary FCN-based salient object detection models against adversarial attacks. The proposed framework adopts a novel idea that first introduces some new generic noise to destroy adversarial perturbations, and then learns to predict saliency maps for input images with the introduced noise. Specifically, our proposed method consists of a segment-wise shielding component, which preserves boundaries and destroys delicate adversarial noise patterns and a context-aware restoration component, which refines saliency maps through global contrast modeling. Experimental results suggest that our proposed framework improves the performance significantly for state-of-the-art models on a series of datasets.
Abstract (translated)
近年来，由于深卷积神经网络能够为图像获取强大的特征，使得突出的目标检测得到了显著的改善。特别是，最先进的突出目标检测方法，从完全卷积网络（FCN）为基础的框架，从端到端的培训和预测像素级标签，具有高精度和高效率。然而，这种框架存在着对抗性攻击，这种攻击通过在输入图像中添加准不可察觉的噪声，而不改变由人类对象注释的地面真相，从而混淆了神经网络。据我们所知，本文是第一篇成功地对突出目标检测模型进行敌方攻击的文章，并验证了敌方样本在多种现有方法中的有效性。此外，本文还提出了一种新的端到端可训练框架，以增强任意基于FCN的突出目标检测模型对敌方攻击的鲁棒性。该框架采用了一种新的思想，首先引入一些新的通用噪声来消除敌方干扰，然后学习用引入的噪声预测输入图像的显著性图。具体地说，我们提出的方法包括一个分段屏蔽组件，它可以保留边界并破坏微妙的对抗噪声模式，以及一个上下文感知恢复组件，通过全局对比度建模来改进显著性地图。实验结果表明，我们提出的框架显著提高了一系列数据集上最先进模型的性能。
URL
https://arxiv.org/abs/1905.03434
