Mobile Edge Computing (MEC) enables resource-constrained Internet of Things (IoT) devices to offload computation-intensive workloads to nearby edge servers, reducing latency and energy consumption. However, wireless offloading exposes transmitted data to eavesdropping, raising serious privacy concerns. Existing physical-layer security techniques either introduce additional overhead or fail to conceal semantic information. This paper proposes a diffusion-based semantic encoding framework for secure and efficient computation offloading in MEC systems. By applying a forward diffusion process, task inputs are transformed into approximately noise-like latent representations whose distribution is statistically close to a standard Gaussian distribution prior to transmission. Authorized edge servers equipped with learned reverse diffusion models can reliably recover task-relevant semantics, while intercepted representations reveal negligible information. We analyze the secrecy and robustness of diffusion-based encoding from an information-theoretic perspective and integrate the proposed encoder-decoder into a practical MEC offloading pipeline. Extensive experiments under realistic wireless conditions demonstrate that diffusion-based encoding significantly improves secrecy, reduces transmission overhead, and maintains high task performance. Compared with autoencoder and variational autoencoder baselines, the proposed approach offers substantially stronger resistance to reconstruction and inference attacks while preserving computational efficiency at the edge.
Digital watermarking has emerged as a critical technique for provenance and copyright attribution in AI-generated imagery, yet its robustness against realistic, model-agnostic removal attacks remains poorly explored. Existing attacks either succeed only against specific generative models or achieve removal at the cost of severe visual degradation. In this paper, we propose MarkNull, a model-agnostic watermark removal attack via on-manifold latent manipulation. MarkNull is grounded in a key observation: watermarked images exhibit a strong statistical dependency between the generated latent representation and the embedded initial noise. To quantify this dependency, we introduce the Noise-Latent Alignment Score (NLAS) and formulate an optimization objective that selectively decorrelates the latent representation from the embedded watermark while preserving semantic fidelity. Extensive evaluations across different categories of watermarking paradigms, including post-hoc, fine-tuning-based, and initial-noise-based schemes, demonstrate that MarkNull reduces average bit accuracy to 53.14%, approaching random-guessing (50%), without perceptible image degradation. To further improve scalability, we propose MarkNull-A, an amortized, optimization-free variant that distills the attack into a single forward pass, achieving 0.50 s/image with modest computational overhead. Notably, our attacks successfully compromise Google's SynthID-Image system while preserving high visual quality and transfer effectively to video watermarking. Finally, we present an attack detection mechanism as a defensive counterpart to MarkNull and MarkNull-A, highlighting the necessity of developing watermark designs resilient to model-agnostic latent-space attacks.
Jie Cao, Qi Li, Zelin Zhang et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
