Improving PRNU Compression Through Preprocessing, Quantization, and Coding

In the last decade, the extremely rapid proliferation of digital devices capable of acquiring and sharing images over the Web has significantly increased the amount of digital images publicly accessible by everyone with Internet access. Despite the obvious benefits of such technological improvements, it is becoming mandatory to verify the origin and trustfulness of such shared pictures. Photo response non-uniformity (PRNU) is the reference signal for forensic investigators when it comes to verifying or identifying which camera device shot a picture under analysis. In spite of this, PRNU is almost a white-shaped noise, thus being very difficult to compress for storage or large scale search purposes, which are frequent investigation scenarios. To overcome the issue, the forensic community has developed a series of compression algorithms. Lately, Gaussian random projections have proved to achieve state-of-the-art performance. In this paper, we propose two additional steps that help improving even more Gaussian random projections compression rate: 1) a decimation preprocessing step tailored at attenuating frequency components in which PRNU traces are already suppressed in JPEG compressed images and 2) a dead-zone quantizer (rather than the commonly used binary one) that enables an entropy coding scheme to save bitrate when storing PRNU fingerprints or sending residuals over a communication channel. Reported results show the effectiveness of proposed improvements, both under controlled JPEG compression and in a real case scenario.