Joint near-lossless compression and watermarking of still images for authentication and tamper localization

A system is presented to jointly achieve image watermarking and compression. The watermark is a fragile one being intended for authentication purposes. The watermarked and compressed images are fully compliant with the JPEG-LS standard, the only price to pay being a slight reduction of compression efficiency and an additional distortion that can be anyway tuned to grant a maximum preset error. Watermark detection is possible both in the compressed and in the pixel domain, thus increasing the flexibility and usability of the system. The system is expressly designed to be used in remote sensing and telemedicine applications, hence we designed it in such a way that the maximum compression and watermarking error can be strictly controlled (near-lossless compression and watermarking). Experimental results show the ability of the system to detect tampering and to limit the peak error between the original and the processed images.     

Copy-move forgery detection and localization by means of robust clustering with J-Linkage

Understanding if a digital image is authentic or not, is a key purpose of image forensics. There are several different tampering attacks but, surely, one of the most common and immediate one is copy-move. A recent and effective approach for detecting copy-move forgeries is to use local visual features such as SIFT. In this kind of methods, SIFT matching is often followed by a clustering procedure to group keypoints that are spatially close. Often, this procedure could be unsatisfactory, in particular in those cases in which the copied patch contains pixels that are spatially very distant among them, and when the pasted area is near to the original source. In such cases, a better estimation of the cloned area is necessary in order to obtain an accurate forgery localization. In this paper a novel approach is presented for copy-move forgery detection and localization based on the J-Linkage algorithm, which performs a robust clustering in the space of the geometric transformation. Experimental results, carried out on different datasets, show that the proposed method outperforms other similar state-of-the-art techniques both in terms of copy-move forgery detection reliability and of precision in the manipulated patch localization.     

A DVB-MHP web browser to pursue convergence between Digital Terrestrial Television and Internet

In the last decade with the growth of Interactive Digital Television (IDTV) we have seen the end of passive television. An example of this trend is Internet access through television by means of the last generation Set Top Boxes (STBs). The chance to enjoy web contents through digital television Set Top Boxes, delivering a satisfying browsing experience across this platform, could provide the opportunity to promote social inclusion and bridging the “digital divide”. In this paper we present WebClimb, a web browser that would pursue an effective integration of Digital Terrestrial Television (DTT) and Internet in the DVB-MHP platform. WebClimb is a Java-based web browser that enables users to browse the web by interacting with an easy to use Graphical User Interface (GUI), driven by a common TV remote control without asking for reformatting such a content on the server side. In addition to this, the main requirement has been to design and develop an MHP browser application to be broadcast through a TV channel and not embedded in a specific device, though it could be too. Experimental results and a comparison with other possible solutions are provided.     

Media trustworthiness verification and event assessment through an integrated framework: a case-study

Multimedia Tools and Applications - Nowadays, information is provided through diverse network channels and, above all, its diffusion occurs in an always faster and pervasive manner. Social Media...     

Regularization of optic flow estimates by means of weighted vectormedian filtering

Vector median filtering has been recently proposed as an effective method to refine estimated velocity fields. Here, the use of a weighted vector median filtering is suggested to improve the regularization of the optic flow field across motion boundaries. Information about the confidence of the estimated pixel velocities is exploited for the choice of the filter weights. Experimental results, on both synthetic and real-world sequences, show the effectiveness of the proposed procedure.     

Adversarial image detection in deep neural networks

Deep neural networks are more and more pervading many computer vision applications and in particular image classification. Notwithstanding that, recent works have demonstrated that it is quite easy to create adversarial examples, i.e., images malevolently modified to cause deep neural networks to fail. Such images contain changes unnoticeable to the human eye but sufficient to mislead the network. This represents a serious threat for machine learning methods. In this paper, we investigate the robustness of the representations learned by the fooled neural network, analyzing the activations of its hidden layers. Specifically, we tested scoring approaches used for kNN classification, in order to distinguish between correctly classified authentic images and adversarial examples. These scores are obtained searching only between the very same images used for training the network. The results show that hidden layers activations can be used to reveal incorrect classifications caused by adversarial attacks.