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In the domain of medical image processing, medical device manufacturersprotect their intellectual property in many cases by shipping only compiledsoftware, i.e. binary code which can be executed but is difficult to beunderstood by a potential attacker. In this paper, we investigate how well thisprocedure is able to protect image processing algorithms. In particular, weinvestigate whether the computation of mono-energetic images and iodine mapsfrom dual energy CT data can be reverse-engineered by machine learning methods.Our results indicate that both can be approximated using only one single sliceimage as training data at a very high accuracy with structural similaritygreater than 0.98 in all investigated cases.

Does Proprietary Software Still Offer Protection of Intellectual Property in the Age of Machine Learning? -- A Case Study using Dual Energy CT Data