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The lack of interpretability, explainability and transparency makes deep learning models untrusted to perform reliably for making critical decisions. Despite their evaluation against disturbances including geometric transformations, occlusion and convolutional noises in the case of DNN-based image classifiers, the evaluation of contour classifiers has only been studied against rigid displacements (rotation and translation). In this paper, we introduce ContourCertif: a new system to certify deep contour classifiers against convolutional attacks. We use the abstract interpretation theory in order to formulate the Lower and Upper Bounds with abstract intervals to support other classes of advanced attacks including filtering.