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Authorship attribution is the science of inferring characteristics of an author from the characteristics of documents written by the author herself/himself. It represents a problem with a long history and a wide range of applications. Recent works in non-traditional authorship attribution domains demonstrate the practicality of automatic analysis of documents based on authorial style. Such analyses are difficult to apply, little is known about types or rates of error, and few best practices are available. In this paper, we show how quantitative techniques based on image similarity search can be profitably exploited for revealing forgery of handwritten corpora. More in details, we explore the case where a document is represented by means of the image of the document itself. Preliminary experimental results conducted on real data and run on a software implementation of the proposed solution, namely WRITINGSIMILARITYSEARCH (WSS), demonstrate the effectiveness of our document classifier and encourage further investigations on this direction.