Physical layer security (PLS) is a fundamental challenge for sixth-generation (6G) wireless networks, particularly in integrated sensing and communication (ISAC) systems, where sensing targets may simultaneously act as potential eavesdroppers. In this paper, we investigate PLS in a reconfigurable intelligent surface (RIS)-assisted cell-free ISAC system, where distributed access points collaboratively serve users and actively sense potential eavesdroppers. We formulate a weighted sum secrecy rate maximization problem through joint ISAC beamforming design. The resulting non-convex problem is first transformed into a semidefinite programming (SDP) formulation and then solved via convex optimization techniques. To further enhance secure communication performance, we extend the framework by incorporating RIS phase shift optimization and propose an alternating optimization algorithm that jointly optimizes active ISAC beamforming and passive RIS configurations. This joint design exploits the controllable wireless propagation environment provided by RISs to enhance legitimate links while suppressing eavesdropping channels. Extensive simulation results demonstrate that the proposed approach significantly outperforms baseline approaches. Specifically, the proposed joint ISAC method improves the communication signal to interference plus noise ratio (SINR) by approximately $1.8$ dB and the sensing signal-noise ratio (SNR) by $4.8$ dB compared to sensing-priority and communication-priority baselines, respectively. Furthermore, the RIS-assisted framework improves a weighted sum secrecy rate gain of approximately $2.2$ dB compared to the frameworks without RIS, validating the proposed framework as a promising solution for secure and spectrum-efficient cell-free ISAC systems in future 6G networks.

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks.