  As adversarial attacks against machine learning models have raised increasing
concerns, many denoising-based defense approaches have been proposed. In this
paper, we summarize and analyze the defense strategies in the form of symmetric
transformation via data denoising and reconstruction (denoted as $F+$ inverse
$F$, $F-IF$ Framework). In particular, we categorize these denoising strategies
from three aspects (i.e. denoising in the spatial domain, frequency domain, and
latent space, respectively). Typically, defense is performed on the entire
adversarial example, both image and perturbation are modified, making it
difficult to tell how it defends against the perturbations. To evaluate the
robustness of these denoising strategies intuitively, we directly apply them to
defend against adversarial noise itself (assuming we have obtained all of it),
which saving us from sacrificing benign accuracy. Surprisingly, our
experimental results show that even if most of the perturbations in each
dimension is eliminated, it is still difficult to obtain satisfactory
robustness. Based on the above findings and analyses, we propose the adaptive
compression strategy for different frequency bands in the feature domain to
improve the robustness. Our experiment results show that the adaptive
compression strategies enable the model to better suppress adversarial
perturbations, and improve robustness compared with existing denoising
strategies.

Machine Learning;Computer Vision and Pattern Recognition