Content-aware cropping is preferable for recipe promo videos where subjects like faces and dishes are often off-center. Center cropping, however, remains a reliable fallback when subject detection fails or confidence is low. The critical metric is moderation coverage, ensuring every generated crop is traceable to its source and undergoes the same safety review as the original content. Content-aware cropping uses subject signals like face boxes or food masks to position the crop window effectively. This approach aligns better with editorial composition but introduces a failure mode if detectors misidentify elements. Evaluating crops requires checking subject coverage, such as retaining the face in an avatar or keeping the food region visible in a dish. It's crucial to maintain a fixture set of challenging compositions to test cropping algorithms rigorously. Furthermore, all derived media, including each image or video frame crop, must be moderated after the source material. This process ensures that changes in context or emphasis due to cropping don't bypass safety checks. Treat reframing as an auditable decision, not just a cosmetic adjustment, and log all relevant metadata for reproducibility. If a detector fails or the subject isn't clearly identifiable, center cropping with subsequent manual review is a more secure option. Consistency in small avatars might outweigh preserving background in certain scenarios, making center crop a better choice then. Content-aware cropping is valuable when compositions are diverse and losing the subject is detrimental, but it requires careful implementation with fallback and flagging. Ultimately, shipping evidence like source hashes and moderation decisions with the image aids in resolving quality disputes.