This paper develops certified defenses for deep neural network (DNN) based content-based image retrieval (CBIR) against adversarial examples (AXs). Previous works put their effort into certified defense for classification to improve certified robustness, which guarantees that no AX to cause misclassification exists around the sample. Such certified defense, however, could not be applied to CBIR directly because the goals of adversarial attack against classification and CBIR are completely different. To develop the certified defense for CBIR, we first define the new certified robustness of CBIR, which guarantees that no AX that changes the ranking results of CBIR exists around the input images. Then, we propose computationally tractable verification algorithms that verify whether a given feature extraction DNN satisfies the certified robustness of CBIR at given input images. Our proposed verification algorithms are achieved by evaluating the upper and lower bounds of distances between feature representations of perturbed and non-perturbed images in deterministic and probabilistic manners. Finally, we propose robust training methods to obtain feature extraction DNNs that increase the number of inputs that satisfy the certified robustness of CBIR by tightening the upper and lower bounds. We experimentally show that our proposed certified defenses can guarantee robustness deterministically and probabilistically on various datasets.