  The input space complexity determines the model's capabilities to extract
their knowledge and translate the space of attributes into a function which is
assumed in general, as a concatenation of non-linear functions between layers.
In the presence of backdoor attacks, the space complexity changes, and induces
similarities between classes that directly affect the model's training. As a
consequence, the model tends to overfit the input set. In this research, we
suggest the D-vine Copula Auto-Encoder (VCAE) as a tool to estimate the latent
space distribution under the presence of backdoor triggers. Since no
assumptions are made on the distribution estimation, like in Variational
Autoencoders (VAE). It is possible to observe the backdoor stamp in
non-attacked categories randomly generated. We exhibit the differences between
a clean model (baseline) and the attacked one (backdoor) in a pairwise
representation of the distribution. The idea is to illustrate the dependency
structure change in the input space induced by backdoor features. Finally, we
quantify the entropy's changes and the Kullback-Leibler divergence between
models. In our results, we found the entropy in the latent space increases by
around 27\% due to the backdoor trigger added to the input
