  Oracle-less machine learning (ML) attacks have broken various logic locking
schemes. Regular synthesis, which is tailored for area-power-delay
optimization, yields netlists where key-gate localities are vulnerable to
learning. Thus, we call for security-aware logic synthesis. We propose ALMOST,
a framework for adversarial learning to mitigate oracle-less ML attacks via
synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe
generator, employing adversarially trained models that can predict
state-of-the-art attacks' accuracies over wide ranges of recipes and key-gate
localities. Experiments on ISCAS benchmarks confirm the attacks' accuracies
drops to around 50\% for ALMOST-synthesized circuits, all while not undermining
design optimization.
