  Pixel-wise prediction with deep neural network has become an effective
paradigm for salient object detection (SOD) and achieved remarkable
performance. However, very few SOD models are robust against adversarial
attacks which are visually imperceptible for human visual attention. The
previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and
then refines the coarse saliency map by the densely connected conditional
random field (CRF). Different from ROSA that relies on various pre- and
post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to
defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD
models on both adversarial and clean images, as well as inference speed. In
general, LeNo consists of a simple shallow noise and noise estimation that
embedded in the encoder and decoder of arbitrary SOD networks respectively.
Inspired by the center prior of human visual attention mechanism, we initialize
the shallow noise with a cross-shaped gaussian distribution for better defense
against adversarial attacks. Instead of adding additional network components
for post-processing, the proposed noise estimation modifies only one channel of
the decoder. With the deeply-supervised noise-decoupled training on
state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works
not only on adversarial images but also on clean images, which contributes
stronger robustness for SOD. Our code is available at
https://github.com/ssecv/LeNo.
