In a privacy-concerned society it is a common challenge to protect the identities of people appearing in a photo or video. A modern approach is to generate surrogate faces to replace the originals. An algorithm called $k$-Same-Net uses generative neural networks to synthesize faces without any visual resemblance to real people. While highly successful, it suffers from low variety and blurriness of the generated faces. Our goal was to improve the quality and stability of this process by applying the latest methods in the field of generative neural networks, namely generative adversarial networks. We compare the quality of faces generated by several different implementations. Due to the difficulties of training generative networks, which are evident from our work, we were unable to achieve satisfactory results. Some of the methods we present are successful in imitating human faces, emotions and even identities. However, we were unsuccessful in finding a set of parameters that would result in convincing new identities based on multiple existing faces.
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