GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
This work addresses a critical gap in certified defenses for machine learning systems against practical, unrestricted semantic transformations, offering a novel framework with broad applicability in real-world scenarios.
The paper tackles the problem of certifying robustness against semantic transformations like defocus blur and pixelate, which lack closed-form expressions, by proposing GSmooth, a generalized randomized smoothing framework with a dimension augmentation strategy and a scalable algorithm using a surrogate network. Experimental results show the approach effectively certifies robustness against multiple semantic transformations and corruptions, outperforming baselines.
Certified defenses such as randomized smoothing have shown promise towards building reliable machine learning systems against $\ell_p$-norm bounded attacks. However, existing methods are insufficient or unable to provably defend against semantic transformations, especially those without closed-form expressions (such as defocus blur and pixelate), which are more common in practice and often unrestricted. To fill up this gap, we propose generalized randomized smoothing (GSmooth), a unified theoretical framework for certifying robustness against general semantic transformations via a novel dimension augmentation strategy. Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation. The surrogate model provides a powerful tool for studying the properties of semantic transformations and certifying robustness. Experimental results on several datasets demonstrate the effectiveness of our approach for robustness certification against multiple kinds of semantic transformations and corruptions, which is not achievable by the alternative baselines.