Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations

• URL: http://arxiv.org/abs/2306.01125v1
• Date: Thu, 1 Jun 2023 20:21:05 GMT
• Title: Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
• Authors: Yang Sui, Zhuohang Li, Ding Ding, Xiang Pan, Xiaozhong Xu, Shan Liu, Zhenzhong Chen
• Abstract summary: We introduce an attack approach designed to effectively degrade the reconstruction quality of Learned Image Compression (LIC) We generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness.
• Score: 69.25683256447044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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