An imperceptible adversarial attack against reconstruction for learned image compression

Learned image compression has achieved better performance than traditional coding methods in terms of rate-distortion performance. However, the robustness of compression models themselves is rarely paid attention to by coding community. In this work, we explore the potential threats of image compression model, and design an imperceptible adversarial perturbation generation method based on gradient optimization. The image with our generated adversarial perturbation will lead to serious distortion on decoder side when the image is reconstructed. Specifically, we use a similar method based on Fast Gradient Sign Method (FGSM) to optimize a noise and generate an adversarial perturbation against image reconstruction. Furthermore, in order to improve the imperceptibility of our attack, we restrict the optimized noise to the high frequency region of the chrominance components of a YUV image, inspired by the characteristics of human vision system (HVS). See Figure 1 for more details. Experiments on four types of popular image compression models show that our adversarial attack can cause serious distortion on decoder side of the model while keeping the perturbation undetectable to human eyes. We hope that our work could arouse the concern of coding community to the robustness and security of AI intelligent coding technology.