REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary
  Objects in Realistic Scenes
Matthew Hull Zijie J. Wang Duen Horng Chau
Deep Learning models, such as those used in an autonomous vehicle are
vulnerable to adversarial attacks where an attacker could place an adversarial
object in the environment, leading to mis-classification. Generating these
adversarial objects in the digital space has been extensively studied, however
successfully transferring these attacks from the digital realm to the physical
realm has proven challenging when controlling for real-world environmental
factors. In response to these limitations, we introduce REVAMP, an easy-to-use
Python library that is the first-of-its-kind tool for creating attack scenarios
with arbitrary objects and simulating realistic environmental factors,
lighting, reflection, and refraction. REVAMP enables researchers and
practitioners to swiftly explore various scenarios within the digital realm by
offering a wide range of configurable options for designing experiments and
using differentiable rendering to reproduce physically plausible adversarial
objects. We will demonstrate and invite the audience to try REVAMP to produce
an adversarial texture on a chosen object while having control over various
scene parameters. The audience will choose a scene, an object to attack, the
desired attack class, and the number of camera positions to use. Then, in real
time, we show how this altered texture causes the chosen object to be
mis-classified, showcasing the potential of REVAMP in real-world scenarios.
REVAMP is open-source and available at https://github.com/poloclub/revamp.