Deep Neural Networks (DNNs) that process images are being widely used for many safety-critical tasks, from autonomous vehicles to medical diagnosis. Currently, DNN correctness properties are defined at the pixel level over the entire input. Such properties are useful to uncover system faults related to sensor noise or adversarial attacks. However, those properties cannot capture features that are relevant to domain-specific entities and reflect richer types of behaviors. To overcome this limitation, we envision the ability to specify properties based on the entities that may be present in image input, capturing their semantics and how they change. Creating such properties today is a difficult task that requires determining where the entities appear in images, defining how each entity can change, and writing a specification that is compatible with each particular V&amp\;V client. We introduce an initial framework structured around those challenges to assist in the generation of \longnewproperties properties automatically by leveraging object detection models to identify entities in images and creating properties based on entity features. Our feasibility study provides initial evidence that the new properties can detect interesting system faults, such as changes in skin color can modify the output of a gender classification network. We conclude by analyzing the potential of the framework to address the gaps in our vision and by outlining possible directions for future work.