Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
This addresses a security vulnerability in image transmission systems, but it is incremental as it builds on existing adversarial attack methods applied to a new domain.
The paper tackles the robustness of Learned Image Compression (LIC) by introducing an imperceptible attack that degrades reconstruction quality, making objects in reconstructed images virtually unrecognizable, with experiments on the Kodak dataset showing effectiveness.
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where any object in the reconstructed images is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Further, leveraging the insensitivity of high-frequency components to human vision, we introduce Imperceptibility Constraint (IC) to ensure that the perturbations remain inconspicuous. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness. In addition, we provide several findings and suggestions for designing future defenses.