CS

Breaking Assumptions: Distinguishing Between Seemingly Identical Items Using Cheap Sensors

Report ID: TR-921-12

Date: 2012-04-00

Pages: 179

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Abstract:

Intuitively, we understand that physical items are distinct --- we are able to pick up and manipulate them. Yet, we often treat similar items as if they are indistinguishable from one another. Undue reliance on the assumption that seemingly identical items can be treated as if they are identical can lead to unintended, negative consequences. Conversely, the ability to distinguish between seemingly identical objects often enables new applications. This thesis examines three artifacts that carry with them false assumptions about their indistinguishability: blank sheets of paper, `anonymous' bubble forms, and loudspeakers.

Blank sheets of paper, e.g. those in a ream of copy paper, are often treated as if they are identical to one another. We develop a new method to identify sheets of paper by measuring their inherently unique surface texture using commodity equipment. This permits a number of applications, e.g. counterfeit currency detection, and has important implications for paper-based elections, which are discussed in detail.

Next, we turn to optical-scan bubble-forms which often rely on the assumption that they do not reveal the respondent's identity. We demonstrate that individuals tend to mark bubbles in distinctive ways, unintentionally conveying their identity. This has important implications for anonymous surveys and the publication of completed ballots after an election. We describe a number of mitigation techniques and procedural changes to limit the risk of accidentally revealing identifying information.

The final artifact, loudspeakers, are increasingly ubiquitous devices designed to accurately reproduce an audio signal. It is commonly assumed that multiple instances of `identical' loudspeakers will generate the same output given identical inputs. This assumption is false. We demonstrate that individual loudspeakers, even those of the same make and model, induce unique distortions on the generated sound, identifying the individual loudspeaker. We develop methods to identify loudspeakers, enabling a new method of device authentication.

By examining the common underlying assumptions of each artifact, we develop a common methodology used in identifying distinguishing features. This general framework is successfully applied to each artifact, suggesting that other seemingly identical objects may become distinguishable in the future.