Selection-channel-aware rich model for Steganalysis of digital images

International audienceFrom the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic selection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-the-art content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover

Gallium(iii) complexes of nota-bis (phosphonate) conjugates as pet radiotracers for bone imaging

Ligands with geminal bis(phosphonic acid) appended to 1,4,7-triazacyclonone-1,4-diacetic acid fragment through acetamide (NOTAM(BP)) or methylenephosphinate (NO2AP(BP)) spacers designed for Ga-68 were prepared. Ga-III complexation is much faster for ligand with methylenephosphinate spacer than that with acetamide one, in both chemical (high reactant concentrations) and radiolabeling studies with no-carrier-added Ga-68. For both ligands, formation of Ga-III complex was slower than that with NOTA owing to the strong out-of-cage binding of bis(phosphonate) group. Radiolabeling was efficient and fast only above 60 degrees C and in a narrow acidity region (pH similar to 3). At higher temperature, hydrolysis of amide bond of the carboxamide-bis(phosphonate) conjugate was observed during complexation reaction leading to Ga-NOTA complex. In vitro sorption studies confirmed effective binding of the Ga-68 complexes to hydroxyapatite being comparable with that found for common bis(phosphonate) drugs such as pamindronate. Selective bone uptake was confirmed in healthy rats by biodistribution studies ex vivo and by positron emission tomography imaging in vivo. Bone uptake was very high, with SUV (standardized uptake value) of 6.19 +/- 1.27 for [Ga-68]NO2AP(BP)) at 60min p.i., which is superior to uptake of Ga-68-DOTA-based bis(phosphonates) and [F-18]NaF reported earlier (SUV of 4.63 +/- 0.38 and SUV of 4.87 +/- 0.32 for [Ga-68]DO3AP(BP) and [F-18]NaF, respectively, at 60min p.i.). Coincidently, accumulation in soft tissue is generally low (e.g. for kidneys SUV of 0.26 +/- 0.09 for [Ga-68]NO2AP(BP) at 60min p.i.), revealing the new Ga-68 complexes as ideal tracers for noninvasive, fast and quantitative imaging of calcified tissue and for metastatic lesions using PET or PET/CT

Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

Virtual ConferenceInternational audienceAfter 2015, CNN-based steganalysis approaches have started replacing the two-step machine-learning-based steganalysis approaches (feature extraction and classification), mainly due to the fact that they offer better performance.In many instances, the performance of these networks depend on the size of the learning database. Until a certain point, the larger the database, the better the results. However, working with a large database with controlled acquisition conditions is usually rare or unrealistic in an operational context. An easy and efficient approach is thus to augment the database, in order to increase its size, and therefore to improve the efficiency of the steganalysis process. In this article, we propose a new way to enrich a database in order to improve the CNN-based steganalysis performance. We have named our technique "pixels-off". This approach is efficient, generic, and is usable in conjunction with other data-enrichment approaches. Additionally, it can be used to build an informed database that we have named "Side-Channel-Aware databases" (SCA-databases)