  Research into adversarial examples (AE) has developed rapidly, yet static
adversarial patches are still the main technique for conducting attacks in the
real world, despite being obvious, semi-permanent and unmodifiable once
deployed.
  In this paper, we propose Short-Lived Adversarial Perturbations (SLAP), a
novel technique that allows adversaries to realize physically robust real-world
AE by using a light projector. Attackers can project a specifically crafted
adversarial perturbation onto a real-world object, transforming it into an AE.
This allows the adversary greater control over the attack compared to
adversarial patches: (i) projections can be dynamically turned on and off or
modified at will, (ii) projections do not suffer from the locality constraint
imposed by patches, making them harder to detect.
  We study the feasibility of SLAP in the self-driving scenario, targeting both
object detector and traffic sign recognition tasks, focusing on the detection
of stop signs. We conduct experiments in a variety of ambient light conditions,
including outdoors, showing how in non-bright settings the proposed method
generates AE that are extremely robust, causing misclassifications on
state-of-the-art networks with up to 99% success rate for a variety of angles
and distances. We also demostrate that SLAP-generated AE do not present
detectable behaviours seen in adversarial patches and therefore bypass
SentiNet, a physical AE detection method. We evaluate other defences including
an adaptive defender using adversarial learning which is able to thwart the
attack effectiveness up to 80% even in favourable attacker conditions.
