Secondary use of clinical data requires removing Protected Health Information (PHI) under regulations such as HIPAA. While de-identification is well studied for text, clinical records are often structured PDFs, where PHI appears across complex layouts, tables, and headers. We introduce Paint-It-Black (PIB), the first benchmark for end-to-end, pixel-level PHI de-identification in clinical PDFs. PIB contains physician-validated synthetic documents with diverse layouts, document types, and controlled visual degradations, annotated with HIPAA-aligned labels and bounding boxes. We evaluate locally deployable Multimodal Large Language Models (MLLMs) and find that even the strongest remain below the reliability required for clinical use. A conventional OCR+NER baseline performs substantially worse than the best MLLMs, highlighting the inherently multimodal nature of clinical PHI de-identification.