Improving PRNU Compression Through Preprocessing, Quantization, and Coding

Abstract

In 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: i) a decimation preprocessing step tailored at attenuating frequency components in which PRNU traces are already suppressed in JPEG compressed images; ii) 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.

On the information leakage quantification of camera fingerprint estimates

“…As already pointed out and formalized in [8], existing techniques in the literature can mitigate the contextual residues of images on the PRNU. Examples are: 1) compression schemes and binarization [9][10][11][12], which are originally conceived to reduce the computational burden in the estimation process and limit the required storage of the resulting fingerprint; 2) the application of linear filters, as high pass filters (both fixed [13][14][15] and trainable [16]) and convolutional neural networks for feature extraction [17], which were found to be useful to enforce neural nets to work with noise residuals [5] in both forgery detection [13,18] and camera attribution [19], and 3) the use of more powerful denoising schemes than the wavelet denoiser. In the present paper, we take a step further in this direction, analyzing empirically the effects of JPEG compression and the use of more powerful denoising schemes, as BM3D [20].…”

A Leak in PRNU Based Source Identification—Questioning Fingerprint Uniqueness

“…To further improve its effectiveness and efficiency, several filters and pre-processing steps have been designed [8], [9], [10], [11], [12], and, recently, also data-driven approaches have been proposed [13], [14]. PRNU compression techniques have been developed to enable very large scales operations [15], [16], previously impossible due to the size of the PRNU pattern and the matching operations' complexity. Such a trace has also been studied under more complicated setups, e.g., when the media is exchanged through social media [17], [18], or when it is acquired using digital zoom [6].…”

Combining PRNU and noiseprint for robust and efficient device source identification

“…The analyst may be required to process a large number of images, for example all images downloaded from a social account, and look for their provenance in a huge database of available cameras (PRNU patterns). This calls for an inordinate processing time, unless suitable methods are used to reduce computation, typically involving some forms of data summarization [4,5,6]. Working on small crops, rather than on the whole image, is a simple and effective way to achieve such a goal.…”