arXiv:2605.29983v1 Announce Type: new Abstract: The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typically rely on computationally expensive explicit regularization. In this work, we show that attribution robustness can arise implicitly from the learning dynamics of standard stochastic gradient descent. We theoretically motivate this effect through connections between parameter-space and input-space curvature, and validate it across architectures, datasets, and attribution methods, with negligible comp
The increasing reliance on deep learning in critical applications necessitates robust and explainable AI, making adversarial robustness a timely research area.
Improving the adversarial robustness of AI attributions without significant computational overhead enhances the trustworthiness and wider adoption of AI systems, especially in sensitive domains.
This research suggests that intrinsically robust attribution methods can be developed through existing learning dynamics, potentially simplifying and accelerating the deployment of explainable and secure AI.
- · AI developers
- · Deep learning researchers
- · Sectors requiring explainable AI (e.g., healthcare, finance)
- · AI ethics and safety organizations
- · Developers of computationally expensive explicit regularization methods
More secure and transparent deep learning models can be developed and deployed with greater ease.
Increased public and institutional trust in AI systems due to their improved explainability and robustness against adversarial attacks.
Accelerated integration of AI into high-stakes decision-making processes, potentially leading to new regulatory frameworks and industry standards for AI explainability.
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Read at arXiv cs.LG