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

Guided Stereo Matching

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.Comment: CVPR 201

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

Se non esistesse un luogo dove stare al sicuro? Terremoto e sublime: dagli scritti pre-critici alla Kritik der Urteilskraft

SINOSSI: Lo spaventoso sisma di Lisbona, verificatosi il 1° novembre 1755, non comportò solo incalcolabili devastazioni sul piano socio-economico, ma andò a colpire profondamente tanto l’immaginario popolare (minandone le certezze quotidiane), quanto i savants dell’Europa settecentesca, stimolando un’intensa riflessione (che coprì l’intero spettro dei punti di vista, da quello teologico, a quello scientifico). Il giovane Kant è chiaro exemplum dell’interesse suscitato dall’eccezionale fenomeno tellurico presso gli illuministi: tra il gennaio e l’aprile del 1756, egli dette alle stampe tre brevi saggi interamente dedicati alla descrizione scientifica del terremoto portoghese, saggi che si inseriscono nel contesto degli studi del pensatore di Königsberg riguardanti la filosofia della natura e, nello specifico, l’età, l’origine e la costituzione della terra (oggetto dei saggi del 1754 e del 1755). Con il presente lavoro, si intende portare all’attenzione del lettore quella che pare essere una precisa evoluzione della concezione dell’utilità del terremoto, nel passaggio dal periodo pre-critico a quello critico, in particolare nella Kritik der Urteilskraft (1790). Qui l’Autore pare escludere il terremoto dall’elenco degli eventi naturali catastrofici la cui esperienza, “se ci troviamo al sicuro”, genera nell’uomo il concetto di sublime dinamico (KU, AA 05: 261). Quali sono le ragioni per cui il terremoto non trova spazio in questo discorso? Il sisma perde cioè i caratteri di catastrofe agli occhi di Kant, oppure è giudicato così terrificante da togliere ogni razionalità all’uomo e impedire ogni spiraglio interpretativo? ABSTRACT: The dreadful Lisbon earthquake, which occurred on 1 November 1755, did not only cause an untold socio- economic devastation, but it also profoundly affected both public imagination (by undermining everyday certainties) and the savants of 18th century Europe, stimulating an intense debate (which covered the entire spectrum of points of view, ranging from the theological to the scientific). Young Kant is a clear exemplum of the interest aroused by this unprecedented seismic phenomenon amongst European thinkers of the Enlightenment: between January and April 1756, he published three brief essays entirely devoted to the scientific description of the Portuguese earthquake, essays that must be understood in the context of his studies on natural philosophy and, in particular, on the age, origin, and formation of the Earth (which were the topics of the essays of 1754 and 1755). With this paper, we aim to draw the Reader’s attention to the evolution that has characterised, in our opinion, Kant’s theses concerning the value and the benefit of the earthquakes, from the pre-critical to the critical period. We intend to focus on the Kritik der Urteilskraft (1790), in which Kant seems to exclude earthquakes from the list of catastrophic natural events, the experience of which generates the concept of dynamic sublime “wenn wir uns nur in Sicherheit befinden” (KU, AA 05: 261). Why do earthquakes have no place in this discourse? Namely, does the earthquake lose its catastrophic essence in Kant’s eyes, or is it judged to be so terrifying that it deprives man of rational thought and suppresses any glimmer of further interpretation

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

Non-Intrusive Underwater Measurement of Local Scour Around a Bridge Pier

: A non-intrusive low-cost technique for monitoring the temporal and spatial evolution of the scour hole around bridge piers is presented. The setup for the application of the technique is simple, low-cost and non-intrusive. It couples a line laser source and commercial camera to get a fast and accurate measurement of the whole scour hole in the front and behind the bridge pier. A short campaign of measurements of the scour hole around a bridge pier in clear-water conditions is presented to provide a control test and to show how to apply the new method. Finally, the results are compared with two of the most used equations, for the time evolution of the maximum scour depth in clear-water conditions, to show the effectiveness of the proposed techniqu

APPERCEPTION, APPERCEVOIR, S' APPERCEVOIR DE. EVOLUTION D'UN TERME ET D'UNE FONCTION COGNITIVE

Apperception is undoubtedly one of the most important terms in Leibniz’s Monadology, both for its neological nature and its central role in the epistemological and psychological Leibnizian thought. I will discuss the reflections made by Leibniz in the Nouveaux Essais on the subject and its cognitive faculties. I will thus concentrate on the French translation of the Lockean Essay concerning Humane Understanding, which led up to the creation of apperception, as substantive, in the context of the 17th and 18th centuries philosophy

WISE: A Semantic and Interoperable Web of Things Architecture for Smart Environments

The rapid proliferation of Internet of Things devices has led to a number of different standards and technologies which offer novel and exciting services. One of the key aspect of the Internet of Things is its ubiquitness, as devices may spontaneously form networks and leave them possibly in short time frames. This is the case of Smart Environments such as Smart Homes, in which users carry a set of devices like wearables and mobile applications to monitor their behavior and provide contextual services. However, the interoperability and seamless interaction of different devices is yet to be fully realized. In this paper we propose WISE, a framework that leverages the Web of Thing architecture and Semantic technologies to overcome technical and conceptual interoperability difficulties and enables the creation of cooperative Smart Environments that self-adapt on the basis of users' preferences. The use of Semantic technologies enables to understand which devices can provide the needed affordances to meet the user preferences, while the WoT architecture is leveraged to access devices in a standardized manner. We also propose a reference implementation based on off-the-shelf devices which demonstrate the feasibility of WISE