  Recent work has introduced attacks that extract the architecture information
of deep neural networks (DNN), as this knowledge enhances an adversary's
capability to conduct black-box attacks against the model. This paper presents
the first in-depth security analysis of DNN fingerprinting attacks that exploit
cache side-channels. First, we define the threat model for these attacks: our
adversary does not need the ability to query the victim model; instead, she
runs a co-located process on the host machine victim's deep learning (DL)
system is running and passively monitors the accesses of the target functions
in the shared framework. Second, we introduce DeepRecon, an attack that
reconstructs the architecture of the victim network by using the internal
information extracted via Flush+Reload, a cache side-channel technique. Once
the attacker observes function invocations that map directly to architecture
attributes of the victim network, the attacker can reconstruct the victim's
entire network architecture. In our evaluation, we demonstrate that an attacker
can accurately reconstruct two complex networks (VGG19 and ResNet50) having
observed only one forward propagation. Based on the extracted architecture
attributes, we also demonstrate that an attacker can build a meta-model that
accurately fingerprints the architecture and family of the pre-trained model in
a transfer learning setting. From this meta-model, we evaluate the importance
of the observed attributes in the fingerprinting process. Third, we propose and
evaluate new framework-level defense techniques that obfuscate our attacker's
observations. Our empirical security analysis represents a step toward
understanding the DNNs' vulnerability to cache side-channel attacks.
