Adversarial Robustness Limits via Scaling-Law and Human-Alignment
  Studies
Brian R. Bartoldson James Diffenderfer Konstantinos Parasyris Bhavya Kailkhura
This paper revisits the simple, long-studied, yet still unsolved problem of
making image classifiers robust to imperceptible perturbations. Taking CIFAR10
as an example, SOTA clean accuracy is about $100$%, but SOTA robustness to
$\ell_{\infty}$-norm bounded perturbations barely exceeds $70$%. To understand
this gap, we analyze how model size, dataset size, and synthetic data quality
affect robustness by developing the first scaling laws for adversarial
training. Our scaling laws reveal inefficiencies in prior art and provide
actionable feedback to advance the field. For instance, we discovered that SOTA
methods diverge notably from compute-optimal setups, using excess compute for
their level of robustness. Leveraging a compute-efficient setup, we surpass the
prior SOTA with $20$% ($70$%) fewer training (inference) FLOPs. We trained
various compute-efficient models, with our best achieving $74$% AutoAttack
accuracy ($+3$% gain). However, our scaling laws also predict robustness slowly
grows then plateaus at $90$%: dwarfing our new SOTA by scaling is impractical,
and perfect robustness is impossible. To better understand this predicted
limit, we carry out a small-scale human evaluation on the AutoAttack data that
fools our top-performing model. Concerningly, we estimate that human
performance also plateaus near $90$%, which we show to be attributable to
$\ell_{\infty}$-constrained attacks' generation of invalid images not
consistent with their original labels. Having characterized limiting
roadblocks, we outline promising paths for future research.