  Deep learning driven joint source-channel coding (JSCC) for wireless image or
video transmission, also called DeepJSCC, has been a topic of interest recently
with very promising results. The idea is to map similar source samples to
nearby points in the channel input space such that, despite the noise
introduced by the channel, the input can be recovered with minimal distortion.
In DeepJSCC, this is achieved by an autoencoder architecture with a
non-trainable channel layer between the encoder and decoder. DeepJSCC has many
favorable properties, such as better end-to-end distortion performance than its
separate source and channel coding counterpart as well as graceful degradation
with respect to channel quality. However, due to the inherent correlation
between the source sample and channel input, DeepJSCC is vulnerable to
eavesdropping attacks. In this paper, we propose the first DeepJSCC scheme for
wireless image transmission that is secure against eavesdroppers, called
DeepJSCEC. DeepJSCEC not only preserves the favorable properties of DeepJSCC,
it also provides security against chosen-plaintext attacks from the
eavesdropper, without the need to make assumptions about the eavesdropper's
channel condition, or its intended use of the intercepted signal. Numerical
results show that DeepJSCEC achieves similar or better image quality than
separate source coding using BPG compression, AES encryption, and LDPC codes
for channel coding, while preserving the graceful degradation of image quality
with respect to channel quality. We also show that the proposed encryption
method is problem agnostic, meaning it can be applied to other end-to-end JSCC
problems, such as remote classification, without modification. Given the
importance of security in modern wireless communication systems, we believe
this work brings DeepJSCC schemes much closer to adoption in practice.
