A Training-Free Defense Framework for Robust Learned Image Compression
Myungseo Song Jinyoung Choi Bohyung Han
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.