  Multi-bit watermarking (MW) has been developed to improve robustness against
signal processing operations and geometric distortions. To this end, benchmark
tools that test robustness by applying simulated attacks on watermarked images
are available. However, limitations in these general attacks exist since they
cannot exploit specific characteristics of the targeted MW. In addition, these
attacks are usually devised without consideration of visual quality, which
rarely occurs in the real world. To address these limitations, we propose a
watermarking attack network (WAN), a fully trainable watermarking benchmark
tool that utilizes the weak points of the target MW and induces an inversion of
the watermark bit, thereby considerably reducing the watermark extractability.
To hinder the extraction of hidden information while ensuring high visual
quality, we utilize a residual dense blocks-based architecture specialized in
local and global feature learning. A novel watermarking attack loss is
introduced to break the MW systems. We empirically demonstrate that the WAN can
successfully fool various block-based MW systems. Moreover, we show that
existing MW methods can be improved with the help of the WAN as an add-on
module.
