  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%.
