  While there has been extensive work on generating physics-based adversarial
samples recently, an overlooked class of such samples come from physical
failures in the camera. Camera failures can occur as a result of an external
physical process, i.e. breakdown of a component due to stress, or an internal
component failure. In this work, we develop a simulated physical process for
generating broken lens as a class of physics-based adversarial samples. We
create a stress-based physical simulation by generating particles constrained
in a mesh and apply stress at a random point and at a random angle. We perform
stress propagation through the mesh and the end result of the mesh is a
corresponding image which simulates the broken lens pattern. We also develop a
neural emulator which learns the non-linear mapping between the mesh as a graph
and the stress propagation using constrained propagation setup. We can then
statistically compare the difference between the generated adversarial samples
with real, simulated and emulated adversarial examples using the detection
failure rate of the different classes and in between the samples using the
Frechet Inception distance. Our goal through this work is to provide a robust
physics based process for generating adversarial samples.
