Towards Analyzing Semantic Robustness of Deep Neural Networks

Despite the impressive performance of Deep Neural Networks (DNNs) on various vision tasks, they still exhibit erroneous high sensitivity toward semantic primitives (e.g. object pose). We propose a theoretically grounded analysis for DNN robustness in the semantic space. We qualitatively analyze different DNNs' semantic robustness by visualizing the DNN global behavior as semantic maps and observe interesting behavior of some DNNs. Since generating these semantic maps does not scale well with the dimensionality of the semantic space, we develop a bottom-up approach to detect robust regions of DNNs. To achieve this, we formalize the problem of finding robust semantic regions of the network as optimizing integral bounds and we develop expressions for update directions of the region bounds. We use our developed formulations to quantitatively evaluate the semantic robustness of different popular network architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust than ResNet50. We hope that this tool will serve as a milestone towards understanding the semantic robustness of DNNs.Comment: Presented at European conference on computer vision (ECCV 2020) Workshop on Adversarial Robustness in the Real World ( https://eccv20-adv-workshop.github.io/ ) [best paper award]. The code is available at https://github.com/ajhamdi/semantic-robustnes

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks

Formal Verification of Neural Network Controlled Autonomous Systems

In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial conditions such that a robot trajectory starting from these initial conditions is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of the safe initial states. The first technical problem in computing the finite state abstraction is to mathematically model the imaging function that maps the robot position to the LiDAR image. To that end, we introduce the notion of imaging-adapted sets as partitions of the workspace in which the imaging function is guaranteed to be affine. We develop a polynomial-time algorithm to partition the workspace into imaging-adapted sets along with computing the corresponding affine imaging functions. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) nonlinearity, the second technical challenge is to analyze the behavior of the neural network. To that end, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible segments of different ReLUs. SMC solvers then use a Boolean satisfiability solver and a convex programming solver and decompose the problem into smaller subproblems. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space feasible ReLU segments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with increasing complexity of the neural network controller

Evaluating the Vulnerability of Mountain Springs: A Case Study in Italy to Prioritize Conservation and Management Strategies

This research introduces a methodology for evaluating the protection zone of vulnerable mountain springs using an hydrogeochemical approach. Mountain springs play a crucial role in maintaining the ecological balance and ensuring the well-being and resilience of communities residing in mountainous areas. These resources frequently serve as the primary freshwater supply in numerous mountainous regions, their impact extends beyond these areas by catering to diverse applications, including agriculture, farming, hydropower generation, artificial snowmaking, and industrial utilization. Despite their importance, mountain springs are under increasing threat due to climate change and human activities and thus need to be preserved and managed to ensure a sustainable use and conservation. In this study, we assess the vulnerability of two mountain springs located in a karstic water system in the Northern Italy mountainous region. Particularly we analyze the hydrogeological and hydrogeochemical parameters of the two mountain springs, together with the oxygen and hydrogen isotopic composition (δ18O and δ2H) and d-excess of both the springs and the rainwater of the area. The considered parameters were continuously measured from September 2018 to September 2021. The main goal is to assess the geochemical and hydrological processes that control the springs water quality and the isotopic composition of precipitation and use them for formulating effective springs protection measures. Our results show that the vulnerability of mountain springs is influenced by various factors that include the use of the resource, the meteorological conditions, and the hydrogeology of the area. We propose a method that integrates the Vulnerability Estimator for Spring Protection Areas index with the use of the water stable isotopes to identify springs’ protection zones that takes in consideration the recharge area of the aquifers feeding the springs. Our study contributes to the development of a framework for assessing the vulnerability of mountain springs and highlights the importance of integrating the geochemical characteristics and the anthropic pressure in the conservation and management of these critical freshwater resources. This study is part of Next Innovation Ecosystem Program "Interconnected Northeast Innovation Ecosystem (iNEST)" supported by the European Union

Use of XR-QA2 radiochromic films for quantitative imaging of a synchrotron radiation beam

This work investigates the use of XR-QA2 radiochromic films for quantitative imaging of a synchrotron radiation (SR) beam. Pieces (200 7 30 mm2) of XR-QA2 film were irradiated in a plane transverse to the beam axis, at the SYRMEP beamline at ELETTRA (Trieste), with a monochromatic beam of size 170 7 3.94 mm2 (H 7 V) and energy of 28, 35, 38 or 40 keV. The response was calibrated in terms of average air kerma (1\uf02d20 mGy), measured with a calibrated ionization chamber. Films were digitized in reflectance mode using a flatbed scanner. The 16-bit red channel was used. The net\uf020reflectance was then converted to photon fluence per unit air kerma (mm-2 mGy-1). The SR beam profile was acquired also with a scintillator (GOS) based, fiberoptic coupled CCD camera as well as with a scintillator based flat panel detector. Horizontal profiles obtained with the two modalities were compared, evaluated in a ROI of 17.71 7 0.59 mm2, across the beam centre. Once corrected for flat field, the CCD profile was scaled in order to have the same average value as the normalized profile acquired with the gafchromic film. The same procedure was followed for the beam images acquired with the flat panel detector. Horizontal and vertical line profiles acquired with the radiochromic film show an uneven 2D distribution of the beam intensity, with variations in the order of 15\uf02d20% in the horizontal direction, while the statistical uncertainties evaluated for the radiochromic dose measurements were 6% at 28 keV. Larger variations up to 64% were observed in the vertical direction. The response of the radiochromic film is comparable to that of the other imaging detectors, within less than 5% variation

NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is safety verification of ACAS Xu networks and the second deals with the safety verification of a deep learning-based adaptive cruise control system

Verisig: verifying safety properties of hybrid systems with neural network controllers

This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system. By composing the network’s hybrid system with the plant’s, we transform the problem into a hybrid system verification problem which can be solved using state-of-theart reachability tools. We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel’s conjecture is true. We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller

The March 2022 exceptional heatwave recorded in the isotopic composition of precipitation at Dome C, East Antarctica

An exceptional heatwave impacted on East Antarctica between March 15 and 19, 2022, causing some of the highest temperature anomalies ever measured on Earth. The heat transport was associated to an atmospheric river bringing a moisture flux from lower latitudes to inner Antarctica. Several locations, from coastal sites to the high Antarctic Plateau, experienced record temperatures. The air temperature measured at Concordia Station by the automatic weather station of the Italian Antarctic national research program (PNRA) reached a maximum of -11.7°C. The temperature signal is imprinted in the oxygen and hydrogen isotopic composition of precipitation: this is what allows paleoclimate reconstructions from the isotopic records in ice cores, although post-depositional processes such as the interactions between snow and atmosphere and within the snow column might affect the pristine isotopic signal. Since 2008, precipitations have been collected daily at Concordia Station for δ18O and δD measurements; the activities have been carried out under the PNRA project WHETSTONE and will continue in the framework of the PNRA project AIR-FLOC. Isotopic values from 2008 to 2021 range between -82.63‰ and -26.97‰ for δ18O and between -595.1‰ and -223.0‰ for δD, while water stable isotope data from February to April 2022, show unprecedented high values (δ18O =-18.97‰, δD=-147.9‰), the highest recorded over the last 15 years, in correspondence to the exceptional temperatures and snow precipitations. Moreover, the daily snowfall collected during the same period reached a cumulative value of ~4.3 mm w.e. representing ~18% of the 2022 cumulative annual value (24.1 mm w.e.)

The isotopic composition of water vapor: from discrete to continuous measurements. A focus on calibration methods

The water residence time in the atmosphere is approximately nine days, the shortest residence time in any major reservoir of the whole water cycle on the planet. Nevertheless, water vapor is a key factor in climate and hydrology due to its dynamic behavior. The isotopic composition of water vapor can highlight several processes of the water cycle that link the water reservoirs to the atmosphere (Galewsky et al., 2016). In the past, the isotopic composition of water vapor was generally inferred from precipitation data, assuming isotopic equilibrium between rain and water vapor. This assumption works well when precipitation is abundant but gives misleading results when precipitation is scarce. A common method to determine the isotopic composition of water vapor is the cryotrapping technique, proposed by Craig et al., (1963). Cryotrapping consists in freezing all the moisture content of the air (to avoid fractionation) and analyze the liquid sample with the regular mass spectrometry technique. This process includes the designing of customized cold traps and usually requires several man-hours due to the long sampling time (2 - 8 hours per sample). With the advent of the laser absorption spectrometry (LAS) technique is now possible to determine the isotopic composition of water vapor with sampling time down to seconds. This novel technique increases our knowledge about the isotopic composition of water vapor and gives a substantial help in our understanding of the water cycle, both on global and local scales. However, the continuous measurement of isotopic composition of water vapor requires a specific method to calibrate the large amount of data resulting as the output of a Cavity Ring-Down Spectroscopy (CRDS) analyzer. This includes the production of vapor with known isotopic composition, determination of the response of the analyzer to different humidity levels and correction of the instrumental drift. In this work, we present a summary of potential calibration techniques for continuous measurements of the isotopic composition of water vapor. The study goes in-depth on the developing of a customized calibration unit for a commercial CRDS analyzer (Picarro L1102i). Continuous measurements will be compared to water vapor samples collected with cryotraps and several continuous measurements will be presented highlighting sub-daily processes in the atmospheric boundary layer