Title: Batch Normalization Is Blind to the First and Second Derivatives of the Loss w.r.t. Features. Abstract: We prove that when we do the Taylor series expansion of the loss function, the BN operation will block the influence of the first-order term and most influence of the second-order term of the loss. This is a potential defect of the BN operation. We also find that such a problem is caused by the standardization phase of the BN operation. We believe that the proof of the blindness of a deep model is of significant value to avoiding systemic collapses of a deep model, although such a blindness does not always makes significant damages in all applications. Experiments show that the BN operation significantly affects feature representations in specific tasks.