Reconstruction-Resistant Image Transmission Using Semantic Communications
Source abstract
Abstract
Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering substantial efficiency gains by prioritizing the transmission of task-relevant meaning over bit-level accuracy. However, while its benefits in bandwidth reduction and intelligent data representation are well established, its potential to provide intrinsic reconstruction resistance without relying on conventional cryptographic mechanisms remains largely unexplored. This paper investigates whether semantic communication system architectures themselves can contribute to intrinsic reconstruction resistance for image transmission. We propose an autoencoder-based semantic communication framework in which images are encoded into latent representations and transmitted over a wireless channel, with decoding performed using architecture-specific neural networks. Unlike traditional secure communication approaches that depend on encryption, the proposed method leverages architectural uniqueness and representation-level abstraction to limit unauthorized reconstruction. To systematically analyze this, we evaluate eight adversarial scenarios encompassing variations in encoder–decoder architecture and initialization, including both matched (worst-case) and maximum mismatched (best-case) conditions. The system is modeled using a standard Alice–Bob–Mallory framework, where an adversary attempts to reconstruct intercepted semantic representations without full architectural knowledge. Performance is evaluated using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for reconstruction quality, alongside semantic accuracy measured via a convolutional neural network (CNN)-based classifier and embedding cosine similarity to assess information leakage. Experimental results demonstrate that architectural mismatches substantially degrade both visual reconstruction and semantic interpretability for unauthorized receivers, while matched configurations enable substantial recovery. It is important to emphasise that the proposed approach does not provide cryptographic confidentiality; rather, it offers architecture-dependent resistance to unauthorised semantic reconstruction under restricted adversarial assumptions. Overall, the results show that semantic communication systems can exhibit intrinsic reconstruction resistance through architecture-dependent latent-space organisation, reducing reliance on additional cryptographic overhead under restricted adversarial assumptions, while also highlighting limitations when adversaries possess full architectural and initialisation knowledge.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wireless Signal Modulation Classification · Wireless Communication Security Techniques · Adversarial Robustness in Machine Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XQ, UK
References (18)
Show source-provided references
1. A Paradigm Shift toward Semantic Communications10.1109/MCOM.001.2200099 ↗
2. Qin, Z., Tao, X., Lu, J., and Li, G.Y. (2022). Semantic Communications: Principles and Challenges. arXiv.
3. Advances and Challenges in Semantic Communications: A Systematic Review
4. Huang, B., Chen, H., Guo, C., Xu, X., Ma, N., and Zhang, P. (2025). Semantic Communication on Digital Wireless Communication Systems. Electronics, 14.10.20944/preprints202501.1228.v1 ↗
5. Mohsin, M.A., Jazib, M., Alam, Z., Khan, M.F., Saad, M., and Jamshed, M.A. (2025, January 8–12). Vision Transformer Based Semantic Communications for Next Generation Wireless Networks. Proceedings of the IEEE International Conference on Communications (ICC), Montreal, QC, Canada.10.1109/ICCWorkshops67674.2025.11162294 ↗
6. Is Semantic Communication Secure? A Tale of Multi-Domain Adversarial Attacks10.1109/MCOM.006.2200878 ↗
7. Yang, Z., Chen, M., Li, G.Y., Yang, Y., and Zhang, Z. (2023). Secure Semantic Communications: Fundamentals and Challenges. arXiv.
8. Jiang, W., and Chen, H. (2021, January 7–11). Semantic Encryption for IoT Communications. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.
9. Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving10.1109/LCOMM.2023.3269768 ↗
10. Lattice-Based Semantic-Aware Searchable Encryption for Internet of Things10.1109/JIOT.2024.3400816 ↗
11. Wang, X., and Peng, F. (2025). Semantic Communication Physical Layer Security Performance Analysis. Electronics, 14.10.3390/electronics14071316 ↗
12. A Survey of Secure Semantic Communications10.1016/j.jnca.2025.104181 ↗
13. Rethinking Wireless Communication Security in Semantic Internet of Things10.1109/MWC.011.2200547 ↗
14. Mu, X., and Liu, Y. (2024, January 9–13). Semantic Communication-Assisted Physical Layer Security over Fading Wiretap Channels. Proceedings of the IEEE International Conference on Communications (ICC), Denver, CO, USA.10.1109/ICC51166.2024.10622708 ↗
15. LeCun, Y., Cortes, C., and Burges, C.J.C. (2026, May 12). The MNIST Database of Handwritten Digits. Available online: http://yann.lecun.com/exdb/mnist/.
16. Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto. Technical Report.
17. Glorot, X., and Bengio, Y. (2010, January 13–15). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy.
18. Kingma, D.P., and Ba, J. (2015, January 7–9). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA.