A computer-implemented method for assessing a robustness of an image classifier, wherein the image classifier detects where objects are located in a vicinity of an at least partially autonomous robot from data received from one ore more video sensors or the classifier determines a state of a manufactured product from captured properties by an optical sensor which captures properties of the manufactured product, wherein the method comprises the steps of: - receiving a sensor signal comprising data from the one or more video sensors or from the optical sensor, - determining an original input image (x org) which depends on said sensor signal, - determining, by the image classifier, a first class that characterizes a classification of said original input image (xorg); - determining an adversarial input image (x adv) with a method of obtaining an adversarial input image (x adv) to the image classifier, - determining, by the image classifier, a second class that characterizes said adversarial input image (x adv) and - determining a robustness value (vu) indicating a vulnerability of the classifier depending on said first class and on said second class, wherein the robustness value (vu) is set to a first value, for example "1", indicating a vulnerability, if said first class is not equal to said second class, and the robustness value (vu) is set to a second value, for example "0", indicating a non-vulnerability, if said first class is equal to said second class; wherein the method for obtaining the adversarial input image (x adv) to the image classifier is obtained from an original input image (x org), and wherein said adversarial input image and said original input image cause the image classifier to classify said original input image (x org) as belonging to a first class (l 0) and said adversarial input image (x adv) as belonging to a second class (l), comprising the steps of: - modifying said original input image (x org) to yield a modified input image (x mod); - projecting said modified input image (x mod) onto a metric ball around said original input image (x org) to yield a projected input image (x proj); and - obtaining said adversarial input image (x adv) depending on said projected input image (x proj), characterized in that said metric is an at least approximate Wasserstein distance ( W D, W D λ) and that said at least approximate Wasserstein distance is a Sinkhorn distance ( W D λ) which differs from said Wasserstein distance ( W D) by an entropic term ( E T), and for any pair of first distribution and second distribution, said entropic term ( E T) characterizes an entropy of a distribution Π that satisfies Π1 n =P, Π T 1 n =Q. - receiving a sensor signal comprising data from the one or more video sensors or from the optical sensor, - determining an original input image (x org) which depends on said sensor signal, - determining, by the image classifier, a first class that characterizes a classification of said original input image (xorg); - determining an adversarial input image (x adv) with a method of obtaining an adversarial input image (x adv) to the image classifier, - determining, by the image classifier, a second class that characterizes said adversarial input image (x adv) and - determining a robustness value (vu) indicating a vulnerability of the classifier depending on said first class and on said second class, wherein the robustness value (vu) is set to a first value, for example "1", indicating a vulnerability, if said first class is not equal to said second class, and the robustness value (vu) is set to a second value, for example "0", indicating a non-vulnerability, if said first class is equal to said second class; wherein the method for obtaining the adversarial input image (x adv) to the image classifier is obtained from an original input image (x org), and wherein said adversarial input image and said original input image cause the image classifier to classify said original input image (x org) as belonging to a first class (l 0) and said adversarial input image (x adv) as belonging to a second class (l), comprising the steps of: - modifying said original input image (x org) to yield a modified input image (x mod); - projecting said modified input image (x mod) onto a metric ball around said original input image (x org) to yield a projected input image (x proj); and - obtaining said adversarial input image (x adv) depending on said projected input image (x proj), - modifying said original input image (x org) to yield a modified input image (x mod); - projecting said modified input image (x mod) onto a metric ball around said original input image (x org) to yield a projected input image (x proj); and - obtaining said adversarial input image (x adv) depending on said projected input image (x proj), characterized in that said metric is an at least approximate Wasserstein distance ( W D, W D λ) and that said at least approximate Wasserstein distance is a Sinkhorn distance ( W D λ) which differs from said Wasserstein distance ( W D) by an entropic term ( E T), and for any pair of first distribution and second distribution, said entropic term ( E T) characterizes an entropy of a distribution Π that satisfies Π1 n =P, Π T 1 n =Q.