  This paper presents AutoMarks, an automated and transferable watermarking
framework that leverages graph neural networks to reduce the watermark search
overheads during the placement stage. AutoMarks's novel automated watermark
search is accomplished by (i) constructing novel graph and node features with
physical, semantic, and design constraint-aware representation; (ii) designing
a data-efficient sampling strategy for watermarking fidelity label collection;
and (iii) leveraging a graph neural network to learn the connectivity between
cells and predict the watermarking fidelity on unseen layouts. Extensive
evaluations on ISPD'15 and ISPD'19 benchmarks demonstrate that our proposed
automated methodology: (i) is capable of finding quality-preserving watermarks
in a short time; and (ii) is transferable across various designs, i.e.,
AutoMarks trained on one layout is generalizable to other benchmark circuits.
AutoMarks is also resilient against potential watermark removal and forging
attacks
