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The input space complexity determines the model's capabilities to extracttheir knowledge and translate the space of attributes into a function which isassumed in general, as a concatenation of non-linear functions between layers.In the presence of backdoor attacks, the space complexity changes, and inducessimilarities between classes that directly affect the model's training. As aconsequence, the model tends to overfit the input set. In this research, wesuggest the D-vine Copula Auto-Encoder (VCAE) as a tool to estimate the latentspace distribution under the presence of backdoor triggers. Since noassumptions are made on the distribution estimation, like in VariationalAutoencoders (VAE). It is possible to observe the backdoor stamp innon-attacked categories randomly generated. We exhibit the differences betweena clean model (baseline) and the attacked one (backdoor) in a pairwiserepresentation of the distribution. The idea is to illustrate the dependencystructure change in the input space induced by backdoor features. Finally, wequantify the entropy's changes and the Kullback-Leibler divergence betweenmodels. In our results, we found the entropy in the latent space increases byaround 27\% due to the backdoor trigger added to the input

Effect of backdoor attacks over the complexity of the latent space distribution