  Growing interest in utilizing the wireless spectrum by heterogeneous devices
compels us to rethink the physical layer security to protect the transmitted
waveform from an eavesdropper. We propose an end-to-end symmetric key neural
encryption and decryption algorithm with a modulation technique, which remains
undeciphered by an eavesdropper, equipped with the same neural network and
trained on the same dataset as the intended users. We solve encryption and
modulation as a joint problem for which we map the bits to complex analog
signals, without adhering to any particular encryption algorithm or modulation
technique. We train to cooperatively learn encryption and decryption algorithms
between our trusted pair of neural networks, while eavesdropper's model is
trained adversarially on the same data to minimize the error. We introduce a
discrete activation layer with a defined gradient to combat noise in a lossy
channel. Our results show that a trusted pair of users can exchange data bits
in both clean and noisy channels, where a trained adversary cannot decipher the
data.
