PRNU-based image classification of origin social network with CNN

A huge amount of images are continuously shared on social networks (SNs) daily and, in most of cases, it is very difficult to reliably establish the SN of provenance of an image when it is recovered from a hard disk, a SD card or a smartphone memory. During an investigation, it could be crucial to be able to distinguish images coming directly from a photo-camera with respect to those downloaded from a social network and possibly, in this last circumstance, determining which is the SN among a defined group. It is well known that each SN leaves peculiar traces on each content during the upload-download process; such traces can be exploited to make image classification. In this work, the idea is to use the PRNU, embedded in every acquired images, as the “carrier” of the particular SN traces which diversely modulate the PRNU. We demonstrate, in this paper, that SN-modulated noise residual can be adopted as a feature to detect the social network of origin by means of a trained convolutional neural network (CNN)

Towards geometrically robust data-hiding with structured codebooks

In this paper we analyze performance of practical robust data-hiding in channels with geometrical transformations. By applying information-theoretic argument we show that performance of a system designed based on both random coding and random binning principles is bounded by the same maximal achievable rate for the cases when communication channel includes geometrical transformations or not. Targeting to provide theoretic performance limits of practical robust data-hiding we model it using a multiple access channel (MAC) with side information (SI) available at one of encoders and present the bounds on achievable rates of reliable communications to such a protocol. Finally, considering template-based and redundant-based design of geometrically robust data-hiding systems, we perform security analysis of their performance and present results in terms of number of trial efforts the attacker needs to completely remove hidden informatio