A Darwinian Genetic Algorithm for State Encoding Based Finite State Machine Watermarking
Matthew Lewandowski, Srinivas Katkoori · 2019
We propose an extended Darwinian Genetic Algorithm that builds upon the traditional genetic algorithm in the efforts to more accurately depict the process of natural selection We demonstrate the capabilities of this algorithm by employing it with an existing state encoding based watermarking system for sequential circuits that was previously hindered by attempting to naïvely solve the subgraph isomorphism problem that arises during the watermark embedding process. Wherein using the proposed approach we are able to demonstrate maximal savings for a 64-bit watermark of 26%, 23%, and 20%, in addition to the 128-bit watermark where maximal savings were 30%, 29%, and 20% over the current state-of-the-art in terms of literals, area, and delay required for synthesizing the watermarked finite state machines, respectively. Additionally, average savings for the 64-bit watermark were 1%, and 2% with 4% overhead and for the 128-bit watermark average savings were 12%, 7%, and 2%, in terms of literals, area, and delay.