# Secure Deep-JSCC Against Multiple Eavesdroppers

arXiv: https://arxiv.org/abs/2308.02892 · HTML: https://mlsanity.com/paper/2308.02892/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020)
- [Deep Learning for Channel Coding via Neural Mutual Information Estimation](https://mlsanity.com/paper/1903.02865/) (2019)
- [Privacy-Aware Communication Over a Wiretap Channel with Generative Networks](https://mlsanity.com/paper/2110.04094/) (2021)

## Built on by (most cited first, 7 of 7)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications](https://mlsanity.com/paper/2310.19460/) (2023)
- [Multi-level Reliability Interface for Semantic Communications over Wireless Networks](https://mlsanity.com/paper/2407.05487/) (2024)
- [Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic Communications System Design](https://mlsanity.com/paper/2502.06095/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications](https://mlsanity.com/paper/2309.08568/) (2023)
- [Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism](https://mlsanity.com/paper/2604.20245/) (2026)

## Abstract (arXiv)

In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.