To prevent fake news images from misleading the public, it is desirable not
only to verify the authenticity of news images but also to trace the source of
fake news, so as to provide a complete forensic chain for reliable fake news
detection. To simultaneously achieve the goals of authenticity verification and
source tracing, we propose a traceable and authenticable image tagging approach
that is based on a design of Decoupled Invertible Neural Network (DINN). The
designed DINN can simultaneously embed the dual-tags, \textit{i.e.},
authenticable tag and traceable tag, into each news image before publishing,
and then separately extract them for authenticity verification and source
tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a
parallel Feature Aware Projection Model (FAPM) to help the DINN preserve
essential tag information. In addition, we define a Distance Metric-Guided
Module (DMGM) that learns asymmetric one-class representations to enable the
dual-tags to achieve different robustness performances under malicious
manipulations. Extensive experiments, on diverse datasets and unseen
manipulations, demonstrate that the proposed tagging approach achieves
excellent performance in the aspects of both authenticity verification and
source tracing for reliable fake news detection and outperforms the prior
works