Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits. With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms. Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack. This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.
This paper is concerned with both security and machine learning. 
Assuming that data is encoded, transmited, and decoded using a VAE,
the paper proposes a man-in-middle attack that alters the VAE encoding of the input data so that the decoded output will be misclassified.
The objectives are to: 1) fool the autoencoder; the classification output of the autoencoder is different from the actual class of the input; 2) make minimal change in the middle so that the attack is not detectable. 
This paper is concerned with both security and machine learning, but there is no clear contributions to either field. From the machine learning perspective, the proposed "attacking" method is standard without any technical novelty. From the security perspective, the scenarios are too simplistic. The encoding-decoding mechanism being attacked is too simple without any security enhancement. This is an unrealistic scenario. For applications with security concerns, there should have been methods to guard against man-in-the-middle attack, and the paper should have at least considered some of them. Without considering the state-of-the-art security defending mechanism, it is difficult to judge the contribution of the paper to the security community. 
I am not a security expert, but I doubt that the proposed method are formulated based on well founded security concepts and ideas. For example, what are the necessary and sufficient conditions for an attacking method to be undetectable? Are the criteria about the magnitude of epsilon given on Section 3.3. necessary and sufficient? Is there any reference for them? Why do we require the correspondence between the classification confidence of tranformed and original data? Would it be enough to match the DISTRIBUTION of the confidence?