Scaling Laws Reveal Fundamental Limits to Adversarial Robustness on CIFAR10

Core Concepts

Adversarial robustness on CIFAR10 has fundamental limits that cannot be overcome even with unlimited compute and data, due to the generation of invalid adversarial images that humans also misclassify.

Abstract

The paper revisits the problem of making image classifiers robust to imperceptible adversarial perturbations, using CIFAR10 as an example. It develops the first scaling laws for adversarial training, which reveal inefficiencies in prior art and provide actionable feedback to advance the field. The key findings are: Scaling model size, dataset size, and synthetic data quality can improve adversarial robustness, but the authors surpass the prior state-of-the-art (SOTA) with 20% fewer training FLOPs and 70% fewer inference FLOPs using a compute-efficient setup. The scaling laws predict that robustness slowly grows and plateaus at around 90%, suggesting that dwarfing the new SOTA by scaling is impractical, and perfect robustness is impossible. The authors carry out a small-scale human evaluation on the adversarial data that fools their top-performing model. They estimate that human performance also plateaus near 90%, which is attributable to the ℓ∞-constrained attacks generating invalid images that are no longer consistent with their original labels. The authors outline promising paths for future research, including the need to rethink attack formulations to only produce valid images that abide by the original label.

Stats

Reaching human-level performance on CIFAR10 adversarial robustness would require roughly 1030 FLOPs, about 3,000 years of TF32 matrix math on 25,000 MI300 or H100 GPUs. The authors' best model achieves 74% AutoAttack accuracy, a 3% gain over the prior SOTA. Humans correctly classify around 90% of the adversarial images that fool the authors' SOTA model.

Quotes

"Adversarial robustness on CIFAR10 has fundamental limits that cannot be overcome even with unlimited compute and data, due to the generation of invalid adversarial images that humans also misclassify." "Reaching human-level performance on CIFAR10 adversarial robustness would require roughly 1030 FLOPs, about 3,000 years of TF32 matrix math on 25,000 MI300 or H100 GPUs." "The authors' best model achieves 74% AutoAttack accuracy, a 3% gain over the prior SOTA." "Humans correctly classify around 90% of the adversarial images that fool the authors' SOTA model."

Key Insights Distilled From

Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies

by Brian R. Bar... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09349.pdf

Deeper Inquiries

How can the attack formulation be improved to only produce valid adversarial images that abide by the original label

To improve the attack formulation and ensure that only valid adversarial images are produced, several strategies can be implemented: Image Validity Constraints: Introduce constraints during the attack generation process to ensure that the perturbed images remain visually consistent with the original label. This can involve incorporating image quality metrics or perceptual similarity measures to guide the perturbation process. Semantic Consistency Checks: Implement checks to ensure that the perturbed image maintains semantic consistency with the original label. This can involve leveraging semantic segmentation or object detection algorithms to validate the content of the perturbed image. Human-in-the-Loop Validation: Introduce a human validation step where human annotators verify the validity of the adversarial images generated by the attack. This can help filter out invalid images that do not align with the original label. Adversarial Training with Validity Constraints: Incorporate image validity constraints into the adversarial training process to encourage the generation of valid adversarial examples that are perceptually similar to the original images. By implementing these strategies, the attack formulation can be enhanced to produce valid adversarial images that adhere to the original label, improving the overall robustness of the model.

What are the implications of invalid adversarial data for the broader field of adversarial robustness beyond the CIFAR10 benchmark

The implications of invalid adversarial data extend beyond the CIFAR10 benchmark and have broader implications for the field of adversarial robustness: Generalizability Concerns: Invalid adversarial data highlights the limitations of current adversarial robustness benchmarks in accurately assessing the true robustness of models. Models that perform well on benchmarks may still be vulnerable to attacks that generate invalid images. Real-World Vulnerabilities: In real-world applications, the presence of invalid adversarial data can lead to misclassifications and errors, posing potential risks in critical systems where robustness is crucial. Algorithmic Bias: Invalid adversarial data can introduce biases in model predictions and decision-making processes, impacting fairness and accountability in AI systems. Research Directions: The identification of invalid adversarial data underscores the need for improved attack formulations and evaluation metrics that consider image validity. Future research can focus on developing more robust and reliable benchmarks that account for the presence of invalid data. By addressing the implications of invalid adversarial data, researchers can enhance the reliability and effectiveness of adversarial robustness techniques across various domains and applications.

How can the insights from this work on scaling laws and the limitations of ℓ∞-norm attacks be applied to improve adversarial robustness in other domains, such as larger-scale datasets or real-world applications

The insights from this work on scaling laws and the limitations of ℓ∞-norm attacks can be applied to improve adversarial robustness in other domains and real-world applications: Large-Scale Datasets: Scaling laws can be leveraged to optimize model and dataset sizes for adversarial training on larger datasets, such as ImageNet. By understanding the trade-offs between model complexity, dataset size, and computational resources, researchers can develop more efficient and effective adversarial defense strategies. Real-World Applications: The findings on invalid adversarial data highlight the importance of considering image validity in adversarial attacks. Researchers can apply these insights to enhance the robustness of AI systems deployed in real-world scenarios, where the presence of invalid data can have significant consequences. Algorithmic Development: By incorporating image validity constraints and human-in-the-loop validation techniques, researchers can improve the reliability and trustworthiness of adversarial defense mechanisms. This can lead to more resilient AI systems that are better equipped to handle adversarial attacks in diverse settings. Overall, the lessons learned from this study can inform the development of more robust and reliable adversarial defense strategies across a wide range of applications and domains.