{"ID":2834011,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02740","arxiv_id":"2512.02740","title":"Adversarial Jamming for Autoencoder Distribution Matching","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.","short_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 adversaria...","url_abs":"https://arxiv.org/abs/2512.02740","url_pdf":"https://arxiv.org/pdf/2512.02740v1","authors":"[\"Waleed El-Geresy\",\"Deniz Gündüz\"]","published":"2025-12-02T13:23:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IT\"]","methods":"[]","has_code":false}