Abstract: A method for training a neural network.

Abstract: A method for creating a machine learning system which is designed for segmentation and object detection in images. The method includes: providing a directed graph; selecting a path through the graph, at least one additional node being selected from this subset, a path through the graph from the input node along the edges via the additional node up to the output node being selected; creating a machine learning system as a function of the selected path; and training the machine learning system created.

Abstract: A computer-implemented method for training a classifier for classifying input signals provided to the classifier. The classifier is configured to obtain an output signal characterizing a classification of the input signal. The method for training includes: providing a set of perturbations; providing a subset of first training samples each comprising an input signal and a corresponding desired output signal from a first dataset of training samples; selecting a first perturbation for an input signal and a corresponding desired output signal from the subset; obtaining a second perturbation; obtaining a first adversarial example by applying the second perturbation to the input signal; adapting the classifier by training the classifier based on the first adversarial example and the corresponding desired output signal to harden the classifier against the second perturbation; replacing the first perturbation in the set of perturbations a linear combination of the first perturbation and the second perturbation.

Abstract: A computer-implemented method for training a classifier. The classifier is configured to classify input signals of digital image data and/or audio data. The training of the classifier is based on a perturbed input signal obtained by applying a perturbation provided from a plurality of perturbations to an input signal provided from a training dataset. The method includes: providing a plurality of initial perturbations; adapting a perturbation from the plurality of initial perturbations to an input signal, wherein the input signal is randomly drawn from the training dataset and the perturbation is adapted to the input signal such that applying the perturbation to the input signal yields a second input signal, which is classified differently than the first input signal; providing a subset of the plurality of initial perturbations as plurality of perturbations; and training the classifier based on the plurality of perturbations.

Abstract: A method for operating e.g. a robot. The method includes the steps: acquiring a sequence of images of a space surrounding the robot within which a potential object is situated; ascertaining, using a first machine learning system, a respective first variable characterizing the potential object as a function of each of the images; ascertaining, using a second machine learning system, at least one second variable that characterizes the potential object as a function of a plurality of the first variables characterizing the potential object; controlling the robot as a function of the second variable characterizing the potential object. A computer program, a device for carrying out the method, and a machine-readable storage element on which the computer program is stored, are also described.

Abstract: A method for measuring the sensitivity of a classifier for digital images against adversarial attacks. The classifier includes at least one neural network. The method includes: providing a digital image for which the sensitivity is to be measured; providing a generator that is trained to map elements of a latent space to a realistic image; obtaining, according to a set of parameters, an element of the latent space; mapping, using the generator, this element to a disturbance in the space of realistic images; perturbing the digital image with this disturbance; determining, using the classifier, a classification result for the perturbed image; determining, from the classification result, the impact of the disturbance on the classification result; optimizing the set of parameters to maximize this impact; and determining, based at least in part on the maximum impact, the sensitivity of the classifier.

Abstract: A method for generating a universal data signal interference for generating a manipulated data signal for deceiving a first machine learning system, which is configured to ascertain a semantic segmentation of a received one-dimensional or multidimensional data signal, The method includes a) ascertaining a training data set that includes pairs of data signals and associated desired semantic segmentations, b) generating the data signal interference, as a function of the data signals of the training data set, of the associated desired semantic segmentation, as well as of estimated semantic segmentations of the data signals acted upon by the data signal interference.

Abstract: A method for automatically generating an artificial neural network that encompasses modules and connections that link those modules, successive modules and/or connections being added to a current starting network. Modules and/or connections that are to be added are selected randomly from a predefinable plurality of possible modules and connections that can be added. A plurality of possible refinements of the current starting network respectively are generated by adding to the starting network modules and/or connections that are to be added. One of the refinements from the plurality of possible refinements is then selected in order to serve as a current starting network in a subsequent execution of the method.

Abstract: A method for ascertaining a suitable network configuration for a neural network.

Abstract: A method for ascertaining a suitable network configuration for a neural network for a predefined application that is determined in the form of training data.

Abstract: A method for generating a manipulated data signal for misleading a first machine learning system, which is designed to ascertain a semantic segmentation of a received one-dimensional or multi-dimensional data signal, the method having the following steps: a) ascertaining a desired semantic segmentation of the manipulated data signal; and b) generating the manipulated signal as a function of the received data signal and the ascertained desired semantic segmentation as well as an estimated semantic segmentation of the manipulated data signal.

Abstract: A method for operating a detector that is set up to check whether a data signal that is supplied to a machine learning system has been manipulated. The machine learning system is first trained in adversarial fashion using a manipulated data signal, the manipulated data signal having been ascertained by manipulation of a training data signal, and the machine learning system being trained to provide in each case the same output signal when the training data signal or the manipulated data signal is supplied to it. The detector is trained using another manipulated data signal that is produced as a function of the trained machine learning system.

Abstract: A method is described for measuring the vulnerability of an AI module to spoofing attempts, including the classification and/or regression onto which the AI module maps the update data set is ascertained as an unperturbed result for a predefined data set in the input space E; at least one perturbation S having a dimensionality d<D is applied to the predefined data set so that at least one perturbed data set results in the input space E; the classification and/or regression onto which the AI module maps the perturbed data set is ascertained as the perturbed result; the deviation of the perturbed result from the unperturbed result is ascertained using predefined metrics; in response to the deviation satisfying a predefined criterion, it is determined that the AI module with regard to the predefined data set is vulnerable to spoofing attempts having a dimensionality d.

Abstract: A method for training an automated learning system includes processing training input with a first neural network and processing the output of the first neural network with a second neural network. The input layer of the second neural network corresponding to the output layer of the first neural network. The output layer of the second neural network corresponding to the input layer of the first neural network. An objective function is determined using the output of the second neural network and a predetermined modification magnitude. The objective function is approximated using random Cauchy projections which are propagated through the second neural network.

Abstract: The disclosure relates to a method for operating a machine learning system with the following steps. First training of the machine learning system depending on training input values provided and respectively associated training output values. Determine a universal adversarial perturbation depending on a specifiable plurality of the training input values. Perturbing each of the specifiable plurality of the training input values by means of the universal adversarial perturbation. Second training of the machine learning system, at least as a function of the perturbed plurality of training input values and a multiplicity of the training input values. The disclosure also relates to a computer program and an apparatus for executing the method and a machine-readable storage element on which the computer program is stored.

Abstract: A method for operating e.g. a robot. The method includes the steps: acquiring a sequence of images of a space surrounding the robot within which a potential object is situated; ascertaining, using a first machine learning system, a respective first variable characterizing the potential object as a function of each of the images; ascertaining, using a second machine learning system, at least one second variable that characterizes the potential object as a function of a plurality of the first variables characterizing the potential object; controlling the robot as a function of the second variable characterizing the potential object. A computer program, a device for carrying out the method, and a machine-readable storage element on which the computer program is stored, are also described.

Abstract: A method for generating a manipulated data signal for misleading a first machine learning system, which is designed to ascertain a semantic segmentation of a received one-dimensional or multi-dimensional data signal, the method having the following steps: a) ascertaining a desired semantic segmentation of the manipulated data signal; and b) generating the manipulated signal as a function of the received data signal and the ascertained desired semantic segmentation as well as an estimated semantic segmentation of the manipulated data signal.

Abstract: A method for generating a manipulated data signal for misleading a first machine learning system, which is designed to ascertain a semantic segmentation of a received one-dimensional or multi-dimensional data signal, the method having the following steps: a) ascertaining a desired semantic segmentation of the manipulated data signal; and b) generating the manipulated signal as a function of the received data signal and the ascertained desired semantic segmentation as well as an estimated semantic segmentation of the manipulated data signal.

Abstract: A method for generating a universal data signal interference for generating a manipulated data signal for deceiving a first machine learning system, which is configured to ascertain a semantic segmentation of a received one-dimensional or multidimensional data signal, The method includes a) ascertaining a training data set that includes pairs of data signals and associated desired semantic segmentations, b) generating the data signal interference, as a function of the data signals of the training data set, of the associated desired semantic segmentation, as well as of estimated semantic segmentations of the data signals acted upon by the data signal interference.

Abstract: A method for generating a manipulated data signal for misleading a first machine learning system, which is designed to ascertain a semantic segmentation of a received one-dimensional or multi-dimensional data signal, the method having the following steps: a) ascertaining a desired semantic segmentation of the manipulated data signal; and b) generating the manipulated signal as a function of the received data signal and the ascertained desired semantic segmentation as well as an estimated semantic segmentation of the manipulated data signal.