Deep neural networks (DNN) are being used in a wide range of applications including safety-critical systems. Several DNN test generation approaches have been proposed to generate fault-revealing test inputs. However, the existing test generation approaches do not systematically cover the input data distribution to test DNNs with diverse inputs, and none of the approaches investigate the relationship between rare inputs and faults. We propose cit4dnn, an automated black-box approach to generate DNN test sets that are feature-diverse and that comprise rare inputs. cit4dnn constructs diverse test sets by applying combinatorial interaction testing to the latent space of generative models and formulates constraints over the geometry of the latent space to generate rare and fault-revealing test inputs. Evaluation on a range of datasets and models shows that cit4dnn generated tests are more feature diverse than the state-of-the-art, and can target rare fault-revealing testing inputs more effectively than existing methods.