# Optimization of Private Semantic Communication Performance: An Uncooperative Covert Communication Method

arXiv: https://arxiv.org/abs/2508.07586 · HTML: https://mlsanity.com/paper/2508.07586/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Bridging Knowledge Graphs to Generate Scene Graphs](https://mlsanity.com/paper/2001.02314/) (2020)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [GRID: Scene-Graph-based Instruction-driven Robotic Task Planning](https://mlsanity.com/paper/2309.07726/) (2023)
- [Retargetable AR: Context-aware Augmented Reality in Indoor Scenes based on 3D Scene Graph](https://mlsanity.com/paper/2008.07817/) (2020)
- [Transformer-aided Wireless Image Transmission with Channel Feedback](https://mlsanity.com/paper/2306.09101/) (2023)
- [Blockchain-aided Secure Semantic Communication for AI-Generated Content in Metaverse](https://mlsanity.com/paper/2301.11289/) (2023)
- [Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications](https://mlsanity.com/paper/2305.07220/) (2023)
- [Secure Semantic Communications: Fundamentals and Challenges](https://mlsanity.com/paper/2301.01421/) (2023)
- [Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering](https://mlsanity.com/paper/2404.13898/) (2024)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts](https://mlsanity.com/paper/2309.02616/) (2023)
- [Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference](https://mlsanity.com/paper/2302.14702/) (2023)
- [Wireless Image Transmission with Semantic and Security Awareness](https://mlsanity.com/paper/2212.00227/) (2022)
- [The Model Inversion Eavesdropping Attack in Semantic Communication Systems](https://mlsanity.com/paper/2308.04304/) (2023)
- [SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems](https://mlsanity.com/paper/2309.01386/) (2023)
- [Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach](https://mlsanity.com/paper/1908.11038/) (2019)
- [Secure Intelligent Reflecting Surface Aided Integrated Sensing and Communication](https://mlsanity.com/paper/2207.09095/) (2022)
- [Securing Semantic Communications with Physical-layer Semantic Encryption and Obfuscation](https://mlsanity.com/paper/2304.10147/) (2023)

## Abstract (arXiv)

In this paper, a novel covert semantic communication framework is investigated. Within this framework, a server extracts and transmits the semantic information, i.e., the meaning of image data, to a user over several time slots. An attacker seeks to detect and eavesdrop the semantic transmission to acquire details of the original image. To avoid data meaning being eavesdropped by an attacker, a friendly jammer is deployed to transmit jamming signals to interfere the attacker so as to hide the transmitted semantic information. Meanwhile, the server will strategically select time slots for semantic information transmission. Due to limited energy, the jammer will not communicate with the server and hence the server does not know the transmit power of the jammer. Therefore, the server must jointly optimize the semantic information transmitted at each time slot and the corresponding transmit power to maximize the privacy and the semantic information transmission quality of the user. To solve this problem, we propose a prioritised sampling assisted twin delayed deep deterministic policy gradient algorithm to jointly determine the transmitted semantic information and the transmit power per time slot without the communications between the server and the jammer. Compared to standard reinforcement learning methods, the propose method uses an additional Q network to estimate Q values such that the agent can select the action with a lower Q value from the two Q networks thus avoiding local optimal action selection and estimation bias of Q values. Simulation results show that the proposed algorithm can improve the privacy and the semantic information transmission quality by up to 77.8% and 14.3% compared to the traditional reinforcement learning methods.