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In this work, we introduce and discuss Quaternion Generative Adversarial Networks, a variant of generative adversarial networks that uses quaternion-valued inputs, weights and intermediate network representations. Quaternionic representation has the advantage of treating cross-channel information carried by multichannel signals (e.g. color images) holistically, while quaternionic convolution has been shown to be less resource-demanding. Standard convolutional and deconvolutional layers are replaced by their quaternionic variants, in both generator and discriminator nets, while activations and loss functions are adapted accordingly. We have succesfully tested the model on the task of detecting byzantine inscriptions in the wild, where the proposed model is on par with a vanilla conditional generative adversarial network, but is significantly less expensive in terms of model size (requires less …