US 12,322,189 B2
Method and generator for generating disturbed input data for a neural network
Fabian Hüger, Wolfenbüttel (DE); Peter Schlicht, Wolfsburg (DE); Nico Maurice Schmidt, Berlin (DE); Feix Assion, Berlin (DE); and Florens Fabian Gressner, Berlin (DE)
Assigned to VOLKSWAGEN AKTIENGESELLSCHAFT, Wolfsburg (DE); and DSPACE GMBH, Paderborn (DE)
Appl. No. 17/619,159
Filed by Volkswagen Aktiengesellschaft, Wolfsburg (DE); and Neurocat GmbH, Berlin (DE)
PCT Filed Jun. 12, 2020, PCT No. PCT/EP2020/066348
§ 371(c)(1), (2) Date Dec. 14, 2021,
PCT Pub. No. WO2020/249758, PCT Pub. Date Dec. 17, 2020.
Claims priority of application No. 10 2019 208 733.7 (DE), filed on Jun. 14, 2019.
Prior Publication US 2022/0358747 A1, Nov. 10, 2022
Int. Cl. G06V 20/58 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06V 20/58 (2022.01) [G06V 10/764 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating disturbed input data for a neural network for analyzing sensor data of a driver assistance system, which sensor data represent a plurality of digital images, the method comprising:
generating a first metric, which indicates a magnitude of a change in the sensor data, wherein two digital images of the sensor data are compared and an image distances value is provided as part of the first metric;
detecting and classifying one or more objects;
generating a second metric, which indicates where a disturbance of the sensor data is directed to, wherein the second metric is directed at a change of a first object of a specific class;
wherein the first metric and the second metric indicate change of the one or more digital images;
generating an optimization problem from a combination of the first metric and the second metric, wherein the optimization problem comprises a loss function for a neural network which comprises as parameters a disturbance parameter and an image resulting from the disturbance according to the second metric, wherein in the optimization problem the minimum of the disturbance parameter is found under the condition that the extent of the change in the generated image relative to the initial image according to the first metric is below a predefined value;
solving the optimization problem using at least one solution algorithm, wherein the solution indicates a target disturbance of the input data; and
generating, using the target disturbance, disturbed input data comprising changed digital images for the neural network.