Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.Comment: 16 pages, 10 figures. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19

Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

A GAMING APPROACH for CULTURAL HERITAGE KNOWLEDGE and DISSEMINATION

In these last years, video games have become one of the most popular entertainment for children/teenagers/adults thanks to their appealing and seductive features and, in this context, the Serious Games (SG) have become an important research field. The most popular SGs in Cultural Heritage (CH) used the historical building like scenario where the game is playing. In this paper we show the procedure to achieve a CH video game where the Cultural Heritage is the main actor and not the scenario of the game. Furthermore, the game is not a SG but an Action-Adventure Game (AAG) or Survival Game (SuG), in a largest heading it can be classified as Entertainment Games (EGs). The novelty of this study is not only in the original application of the CH within the AAG sector but also consists of the experimentation of the Virtual Reality (VR) algorithm and of the application of Augmented Reality (AR) within the VR scenario used in the form of an avatar. Furthermore, in this paper we overcome the technical problems due to the different size of the environment and the work art

Methods for Procedural Terrain Generation

Procedural generation has been utilized in the automatic generation of data for a long time. This automated processing has been utilized in the entertainment industry as well as in research work in order to be able to quickly produce large amounts of just the kind of data needed, for example, in system testing. In this thesis, we examine different ways to utilize procedural generation to produce different synthetic terrains. First, we will take a closer look at what procedural generation is, where it originally started, and where it was utilized. From this we move on to look at how this technology is utilized in the creation of terrains and what terrain is generally visually required. From this we move on to look at different ways to implement terrain generation. As part of this thesis, we have selected three methods and implemented our own implementations for terrain generation. We look at the performance of these implementations, and what a test group thinks about those synthetic terrains. The results obtained from this are analyzed and presented at the end of the thesis.Proseduraalista generointia on hyödynnetty datan automaattisessa tuottamisessa jo pitkään. Tätä automatisoitua prosessointia on niin hyödynnetty viihdeteollisuudessa kuin tutkimustyössä, jotta ollaan voitu tuottaa nopeasti suuria määriä juuri sellaista dataa kuin tarvitaan esimerkiksi järjestelmän testauksessa. Tässä tutkielmassa tarkastellaan erilaisia tapoja hyödyntää proseduraalista generointia erilaisten synteettisten maastojen tuottamiseksi. Aluksi tutustutaan hieman tarkemmin siihen mitä proseduraalinen generointi on, mistä se on alunperin lähtenyt ja mihin sitä on hyödynnetty. Tästä siirrytään tarkastelemaan miten kyseistä tekniikkaa hyödynnetään maastojen luomisessa ja mitä maastoilta yleensä visuaalisesti vaaditaan. Tästä siirrytään tarkastelemaan eri tapoja toteuttaa maaston generointia. Osana tätä tutkielmaa, on valittu kolme menetelmää ja laadittu niistä kullekin oma toteutus maaston generointiin. Työssä tarkastellaan näiden toteutusten suoritustuloksia, ja mitä mieltä testiryhmä on kyseisistä synteettisistä maastoista. Saadut tulokset ja niiden analyyysi esitellään tutkielman lopussa