  Autoencoder can give rise to an appropriate latent representation of the
input data, however, the representation which is solely based on the intrinsic
property of the input data, is usually inferior to express some semantic
information. A typical case is the potential incapability of forming a clear
boundary upon clustering of these representations. By encoding the latent
representation that not only depends on the content of the input data, but also
the semantic of the input data, such as label information, we propose an
enhanced autoencoder architecture named semantic autoencoder. Experiments of
representation distribution via t-SNE shows a clear distinction between these
two types of encoders and confirm the supremacy of the semantic one, whilst the
decoded samples of these two types of autoencoders exhibit faint dissimilarity
either objectively or subjectively. Based on this observation, we consider
adversarial attacks to learning algorithms that rely on the latent
representation obtained via autoencoders. It turns out that latent contents of
adversarial samples constructed from semantic encoder with deliberate wrong
label information exhibit different distribution compared with that of the
original input data, while both of these samples manifest very marginal
difference. This new way of attack set up by our work is worthy of attention
due to the necessity to secure the widespread deep learning applications.
