TL;DR
This paper reveals vulnerabilities in learned image compression systems to high-resolution global semantic manipulation attacks and proposes a novel method to perform such attacks effectively.
Contribution
It introduces a new attack schedule enabling high-resolution global semantic manipulation in learned image compression, exposing a critical security vulnerability.
Findings
First to achieve stable high-resolution GSM attacks on LIC
Demonstrates LIC systems are vulnerable to high-res semantic manipulations
Provides a new attack method with empirical validation
Abstract
Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing redundancy and achieving superior rate-distortion (RD) performance in benign settings. Unfortunately, due to inherent vulnerabilities in DNNs, LIC systems are susceptible to adversarial perturbations that lead to downstream deterioration, compression rate degradation, untargeted distortion, and both local semantic manipulation (LSM) and low-resolution () global semantic manipulation (GSM). However, high-resolution GSM remains unexplored due to its intractability. Notably, the existing project gradient descent (PGD) method achieves near-perfect white-box attacks for classification, segmentation, and other tasks, yet fails to generalize to high-resolution GSM. Our theoretical and empirical analyses reveal that…
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