Due to the prevalent application of machine learning (ML) techniques and the intrinsic black-box nature of ML models, the need for good explanations that are sufficient and necessary towards a model’s prediction has been well recognized and emphasized. Existing explanation approaches, however, favor either the sufficiency or necessity. To fill this gap, we present DDImage, a technique and tool that automatically produces explanations preserving dual properties for ML-based image classifiers. The core idea behind DDImage is to discover an appropriate explanation by debugging the given input image via a series of image reductions, with respect to the sufficiency and necessity properties. We conduct comprehensive experiments to compare our approach against two state-of-the-art approaches, BayLIME and SEDC, on widely-used models and datasets. The results show that our approach outperforms the other methods in producing minimal explanations preserving both sufficiency and necessity, and it matches or exceeds the other methods in terms of stability.