Effective Universal Unrestricted Adversarial Attacks using a MOE Approach

Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters that are available in several image processing libraries, modern cameras, and mobile applications. The multi-objective optimization takes into account not only the attack success rate but also the detection rate. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks

Micro, Meso, and Macro Data Collection and Analysis, as a Method for Speculative and Artistic Exploration

In this work, an attempt is made to explore the emerging computationally-enhanced private and public environments by analyzing their ecological transitions and its implications on practical, aesthetic, and speculative dimensions. The author has decided to methodologically dissect the multiplicity of information that exists on many possible-to-detect scales (micro, meso, macro), and utilize this extraction as a tool for experimentation and redefinition. With the use of custom-made hardware and software utilities (sensor devices, sentiment analysis algorithms, online APIs, and many more), a vast amount of data is collected and used as a multidimensional layered architecture that constantly shifts and transforms. The extracted and analyzed content of the collection becomes the essence of the work that is shaped and refined through digital and physical making – middleware, recursion, mapping – and by utilizing technological objects within the physical space, the creative process is augmented and amplified, exploring not only new practices and novel applications, but rather redefining behavior, thought-process, and context

Planning under Uncertainty in Linear Time Logic

The “planning as satisfiability” approach for classical planning establishes a correspondence between planning problems and logical theories, and, consequently, between plans and models. This work proposes a similar framework for contingency planning: considering contingent planning problems where the sources of indeterminism are incomplete knowledge about the initial state, non-inertial fluents and nondeterministic actions, it shows how to encode such problems into Linear Time Logic. Exploiting the semantics of the logic, and the notion of conditioned model introduced in this work, a formal characterization is given of the notion of contingent plan (a plan together with the set of conditions that ensure its executability)

Linear temporal logic as an executable semantics for planning languages

This paper presents an approach to artificial intelligence planning based on linear temporal logic (LTL). A simple and easy-to-use planning language is described, PDDL-K (Planning Domain Description Language with control Knowledge), which allows one to specify a planning problem together with heuristic information that can be of help for both pruning the search space and finding better quality plans. The semantics of the language is given in terms of a translation into a set of LTL formulae. Planning is then reduced to “executing” the LTL encoding, i.e. to model search in LTL. The feasibility of the approach has been successfully tested by means of the system Pdk, an implementation of the proposed method

Planning with Graded Fluents and Actions

This work can be seen as a rst approach to a new planning model that takes into account the possibility to express actions and uents with non-boolean values. According to this model, a planning problem is dened using both graded (multi-valued) and classical (boolean) uents. Moreover, actions that can have different application degrees can be defined. In this work a PDDL extension allowing to describe such new problems is proposed and a planning algorithm for such problems is presented

Towards a Parallel Search Engine for Planning Systems Based on Linear Time Logic

A planning problem can be entirely encoded as a set of linear temporal logic (LTL) formulae, in such a way that planning is reduced to model search. In order for this approach to be effective, it is important to enhance the performances of LTL provers. In this work, we study a parallel algorithm for LTL model search, based on the tableaux calculus. In paritcular, the approach presented here is based on the “divide et impera” approach: a task in tableaux construction is identified that can be split into smaller homogeneous processes. The parallelization acts during the construction of each time state: the set of formulas to be expanded is split into k disjoint subsets (where k is the number of processes), the k tableaux expansions are carried out in parallel, and the k results are suitably combined. First promising experimental results are also presented: they are based on the algorithm implementation on a cluster of non homogeneous machines