Computational Framework for Marking Physical Objects Against Counterfeiting and Tampering

Principal Investigator: Daniel Aliaga

Our broad objective is to create a science for embedding information in physical objects whose manufacturing process is inherently imprecise. This is in sharp contrast with the existing body of related computer science which deals with digital objects that are assumed to be created and replicated with zero error. While the marking of digital objects is a well-explored area, the creation of algorithms for placing marks in physical objects is mostly unexplored territory and has the potential to grow to a new and significantly important field. Counterfeiting is a growing economic problem that has been called the “crime of the century” by a recent manufacturing industry report, and its yearly cost is rapidly escalating (its cost to the automotive industry alone is in the tens of billions of dollars and the loss of about 250,000 U.S. jobs for the legitimate manufacturers).

The problem we address and our proposed work are inherently multidisciplinary, combining information hiding, computer vision/graphics, and robust algorithms. While our method affects the manufacturing of physical objects, our focus is on the development of the computational algorithms necessary to accomplish our objectives. A solution must not increase manufacturing cost and must be usable with the current manufacturing pipeline, i.e., the manufacture of counterfeit-resistant objects must use the same physical machinery and processes as before.



Other PIs: Mikhail Atallah