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Robustness certification, which aims to formally certify the predictions ofneural networks against adversarial inputs, has become an integral part ofimportant tool for safety-critical applications. Despite considerable progress,existing certification methods are limited to elementary architectures, such asconvolutional networks, recurrent networks and recently Transformers, onbenchmark datasets such as MNIST. In this paper, we focus on the robustnesscertification of scene text recognition (STR), which is a complex andextensively deployed image-based sequence prediction problem. We tackle threetypes of STR model architectures, including the standard STR pipelines and theVision Transformer. We propose STR-Cert, the first certification method for STRmodels, by significantly extending the DeepPoly polyhedral verificationframework via deriving novel polyhedral bounds and algorithms for key STR modelcomponents. Finally, we certify and compare STR models on six datasets,demonstrating the efficiency and scalability of robustness certification,particularly for the Vision Transformer.

STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers