Summary of Adversarial Robustness Limits Via Scaling-law and Human-alignment Studies, by Brian R. Bartoldson et al.
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
by Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the long-standing issue of making image classifiers robust to imperceptible perturbations. Despite achieving high accuracy on clean datasets like CIFAR10, current state-of-the-art (SOTA) methods struggle to maintain robustness against {}-norm bounded perturbations, with a gap of around 30% between SOTA clean and robust accuracies. To understand this disparity, the authors analyze how model size, dataset size, and synthetic data quality impact robustness by developing scaling laws for adversarial training. These laws reveal inefficiencies in prior art and provide actionable feedback to advance the field. For instance, they discovered that SOTA methods deviate significantly from compute-optimal setups, using excess compute for their level of robustness. By adopting a compute-efficient setup, the authors surpass the prior SOTA with 20% fewer training FLOPs and achieve an AutoAttack accuracy of 74%. However, scaling laws predict that robustness growth slows and plateaus at around 90%, making it impractical to reach perfect robustness. To better understand this predicted limit, the authors conduct a small-scale human evaluation on the AutoAttack data, finding that human performance also plateaus near 90% due to {}-constrained attacks generating invalid images inconsistent with their original labels. The paper outlines promising paths for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make image classifiers work better when they’re not perfect. Imagine you have a super accurate model that can tell apart different types of pictures, but it fails when the pictures are slightly changed in ways that are hard to notice. This is a big problem because we want our models to be good at recognizing things even if there’s a little noise or distortion. To solve this issue, the authors studied how different things like model size and dataset quality affect how well a model can handle these imperfections. They found that current methods use more computer power than they need to get their level of accuracy. By using less computer power, they were able to make their models even better at recognizing pictures, with some models getting 74% accurate on really hard tests. However, the authors think that there’s a limit to how good these models can be because some imperfections are just too hard for humans or computers to recognize. |
Keywords
» Artificial intelligence » Scaling laws » Synthetic data
