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Enhancing sensor pattern noise for source camera identification: an empirical evaluation

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Abstract

The sensor pattern noise (SPN) based source camera identification technique has been well established. The common practice is to subtract a denoised image from the original one to get an estimate of the SPN. Various techniques to improve SPN's reliability have previously been proposed. Identifying the most effective technique is important, for both researchers and forensic investigators in law enforcement agencies. Unfortunately, the results from previous studies have proven to be irreproducible and incomparable dash there is no consensus on which technique works the best. Here, we extensively evaluate various ways of enhancing the SPN by using the public Dresden database. We identify which enhancing methods are more effective and offer some insights into the behavior of SPN. For example, we find that the most effective enhancing methods share a common strategy of spectrum flattening. We also show that methods that only aim at reducing the contamination from image content do not lead to satisfying results, since the non-unique artifacts (NUA) among different cameras are the major troublemaker to the identification performance. While there is a trend of employing sophisticate methods to predict the impact of image content, our results suggest that more effort should be invested to tame the NUAs.