  Deep neural network-based image compression has been extensively studied.
However, the model robustness which is crucial to practical application is
largely overlooked. We propose to examine the robustness of prevailing learned
image compression models by injecting negligible adversarial perturbation into
the original source image. Severe distortion in decoded reconstruction reveals
the general vulnerability in existing methods regardless of their settings
(e.g., network architecture, loss function, quality scale). A variety of
defense strategies including geometric self-ensemble based pre-processing, and
adversarial training, are investigated against the adversarial attack to
improve the model's robustness. Later the defense efficiency is further
exemplified in real-life image recompression case studies. Overall, our
methodology is simple, effective, and generalizable, making it attractive for
developing robust learned image compression solutions. All materials are made
publicly accessible at https://njuvision.github.io/RobustNIC for reproducible
research.
