6 research outputs found
Benford's law: what does it say on adversarial images?
Convolutional neural networks (CNNs) are fragile to small perturbations in
the input images. These networks are thus prone to malicious attacks that
perturb the inputs to force a misclassification. Such slightly manipulated
images aimed at deceiving the classifier are known as adversarial images. In
this work, we investigate statistical differences between natural images and
adversarial ones. More precisely, we show that employing a proper image
transformation and for a class of adversarial attacks, the distribution of the
leading digit of the pixels in adversarial images deviates from Benford's law.
The stronger the attack, the more distant the resulting distribution is from
Benford's law. Our analysis provides a detailed investigation of this new
approach that can serve as a basis for alternative adversarial example
detection methods that do not need to modify the original CNN classifier
neither work on the raw high-dimensional pixels as features to defend against
attacks
Generative modeling of autonomous robots and their environments using reservoir computing
Abstract. Autonomous mobile robots form an important research topic in the field of robotics due to their near-term applicability in the real world as domestic service robots. These robots must be designed in an efficient way using training sequences. They need to be aware of their position in the environment and also need to create models of it for deliberative planning. These tasks have to be performed using a limited number of sensors with low accuracy, as well as with a restricted amount of computational power. In this contribution we show that the recently emerged paradigm of Reservoir Computing (RC) is very well suited to solve all of the above mentioned problems, namely learning by example, robot localization, map and path generation. Reservoir Computing is a technique which enables a system to learn any time-invariant filter of the input by training a simple linear regressor that acts on the states of a highdimensional but random dynamic system excited by the inputs. In addition, RC is a simple technique featuring ease of training, and low computational and memory demands. Keywords: reservoir computing, generative modeling, map learning, T-maze task, road sign problem, path generation 1
Modular neural network and classical reinforcement learning for autonomous robot navigation: Inhibiting undesirable behaviors
Abstract — Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures. R I