Adversarial Jamming for Autoencoder Distribution Matching

• URL: http://arxiv.org/abs/2512.02740v1
• Date: Tue, 02 Dec 2025 13:23:25 GMT
• Title: Adversarial Jamming for Autoencoder Distribution Matching
• Authors: Waleed El-Geresy, Deniz Gündüz,
• Abstract summary: We propose the use of adversarial wireless jamming to regularise the latent space of an autoencoder.<n>We achieve distribution matching comparable to standard variational autoencoders and to Wasserstein autoencoders.
• Score: 45.91787704667618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the use of adversarial wireless jamming to regularise the latent space of an autoencoder to match a diagonal Gaussian distribution. We consider the minimisation of a mean squared error distortion, where a jammer attempts to disrupt the recovery of a Gaussian source encoded and transmitted over the adversarial channel. A straightforward consequence of existing theoretical results is the fact that the saddle point of a minimax game - involving such an encoder, its corresponding decoder, and an adversarial jammer - consists of diagonal Gaussian noise output by the jammer. We use this result as inspiration for a novel approach to distribution matching in the latent space, utilising jamming as an auxiliary objective to encourage the aggregated latent posterior to match a diagonal Gaussian distribution. Using this new technique, we achieve distribution matching comparable to standard variational autoencoders and to Wasserstein autoencoders. This approach can also be generalised to other latent distributions.

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