In the recent years, the global penetration of Internet and the rapid spread of mobile devices have led to an exponential rise of trade in counterfeit and pirated goods with a negative impact on the profits of affected firms and consequently damage for employment and economic growth. This peculiar online trade has taken place mostly on deep web, but today it has started to shift to common IM platform and image based social networks. Regard to this context, this work presents a specific multimedia analytics platform, that monitors image catalogues promoting potential counterfeit products on social networks in order to extract useful information (as email, WeChat or WhatsApp, external links to specific online marketplaces) and profile the potential fakers. The preliminary results, derived by considering the image catalogues shared by various sellers on image based social networks, show the effectiveness of the proposed multimedia analytics methodologies.
This work proposes a method for an effective and quick monitoring of video contents produced by TV Broadcasters by means of a fully automatic system. The proposed system performs acquisition, recognition and classification of logos labeling video contents hosted by video-sharing platforms. This challenge is addressed in the Laguerre-Gauss wavelet domain; as soon as a logo is located, in any area of the video screen, a detection strategy, based on the analysis of local Fisher information of the selected logo region, is applied. A distance metric on the LG saliency maps, based nearest neighbor algorithm, is defined, to classify the logo in the relevant video portion. A preliminary test on a dataset of 300 heterogeneous videos, produced by several European Broadcasters, was performed, to verify the effectiveness of the proposed method. The experimental results proved the robustness of the implemented logo recognition and classification method, also for video content labeled with different logo sizes and shapes and for video content corrupted by geometric transformations and/or coding degradations.