Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector

A reliable order-statistics-based approximate nearest neighbor search algorithm

We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors. Data vectors are classified based on the index and sign of their largest components, thereby partitioning the space in a number of cones centered in the origin. The query is itself classified, and the search starts from the selected cone and proceeds to neighboring ones. Overall, the proposed algorithm corresponds to locality sensitive hashing in the space of directions, with hashing based on the order of components. Thanks to the statistical features emerging through ordering, it deals very well with the challenging case of unstructured data, and is a valuable building block for more complex techniques dealing with structured data. Experiments on both simulated and real-world data prove the proposed algorithm to provide a state-of-the-art performance

Do GANs leave artificial fingerprints?

In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real ones. Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and multimedia forensic countermeasures are in urgent need. In this work, we show that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern. Source identification experiments with several popular GANs show such fingerprints to represent a precious asset for forensic analyses

Analysis of adversarial attacks against CNN-based image forgery detectors

With the ubiquitous diffusion of social networks, images are becoming a dominant and powerful communication channel. Not surprisingly, they are also increasingly subject to manipulations aimed at distorting information and spreading fake news. In recent years, the scientific community has devoted major efforts to contrast this menace, and many image forgery detectors have been proposed. Currently, due to the success of deep learning in many multimedia processing tasks, there is high interest towards CNN-based detectors, and early results are already very promising. Recent studies in computer vision, however, have shown CNNs to be highly vulnerable to adversarial attacks, small perturbations of the input data which drive the network towards erroneous classification. In this paper we analyze the vulnerability of CNN-based image forensics methods to adversarial attacks, considering several detectors and several types of attack, and testing performance on a wide range of common manipulations, both easily and hardly detectable

Autoencoder with recurrent neural networks for video forgery detection

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security, and Forensics, January 201

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Multimodality treatment of unresectable hepatic metastases from pancreatic glucagonoma

Glucagonomas are pancreatic islet cell tumors arising from the alpha cells which belong to neuroendocrine tumors. They frequently metastasize to the liver. We report the case of a 52- year old man with a pancreatic glucagonoma with synchronous multiple liver metastases treated by surgery, transarterial chemoembolization, percutaneous radiofrequency thermal ablation and long-acting octreotide. Our report confirms that a multimodal approach is very effective in patients with unresectable liver metastases from pancreatic endocrine tumors providing long-lasting palliation and probably prolonging survival

Evaluating the influence of electoral violence on democratic consolidaton in Sub-Saharan Africa: the case of the democratic republic of congo from 2006-2018

The democratic legitimacy of African executives has been called into question substantially over the last decade. Using the Democratic Republic of Congo (DRC) as a case study, this evaluative research seeks to analyse how African executives and political elites continue to play a crucial role in inducing civil unrest and electoral violence. For African democracy to develop, there must be enforced resolutions to eradicate issues facing both procedural and substantive democracy on the continent. This research examined electoral violence and unconstitutional acts that aggravate electoral system abuse and diminish the consolidation of democracy in sub-Saharan African states, in particular the DRC. A specific focus on the leadership of Joseph Kabila in the DRC forms the case study component of this research. In 2016, elections were postponed in the DRC and the DRC’s constitutional court interpreted Article 70 and Article 73 of the constitution in a manner that allowed President Kabila to remain in office until a newly elected president was installed. The court’s ruling and interpretation of Article 70 and Article 73 was an attempt to avoid a power vacuum. The study evaluated the components that trigger the escalation of electoral violence in Sub-Saharan African states. The study reports on different contributory factors, including but not limited to, the impact of predatory and rent-seeking leadership towards electoral manipulation; and the effect of patron-client relations on democratic institutions. Even though elections are not the only indicator of democracy stability in a state, this study demonstrated how electoral violence threatens the consolidation of democracy in sub-Saharan African states, in particular the DRC. In examining electoral violence, a desktop analysis method, which was used in the study, involved the collection of data from existing resources in order to provide a more critical lens to understanding electoral violence in the DRC. The theoretical analysis used in the study is the Höglund (2009) framework on electoral institutions which outlined how political violence remains a pervasive feature in Sub-Saharan countries by linking the framework to patron-clientelism.Thesis (MA) -- Faculty of Humanities, School of Governmental and Social Sciences, 202