We study the robustness of learned image compression models against
adversarial attacks and present a training-free defense technique based on
simple image transform functions. Recent learned image compression models are
vulnerable to adversarial attacks that result in poor compression rate, low
reconstruction quality, or weird artifacts. To address the limitations, we
propose a simple but effective two-way compression algorithm with random input
transforms, which is conveniently applicable to existing image compression
models. Unlike the na\"ive approaches, our approach preserves the original
rate-distortion performance of the models on clean images. Moreover, the
proposed algorithm requires no additional training or modification of existing
models, making it more practical. We demonstrate the effectiveness of the
proposed techniques through extensive experiments under multiple compression
models, evaluation metrics, and attack scenarios.

通过基于简单图像转换函数的非训练型防御技术，研究了学习型图像压缩模型在面对对抗攻击时的稳健性，并提出了一种简单但有效的双向压缩算法，可方便地应用于现有的图像压缩模型，并且在保留对干净图像模型的原始失真率性能的同时，无需额外的训练或修改现有模型，更加实用。