Method for measuring the portions of an input image on which an image classifier bases its decision regarding the assignment of this input image to one or more classes of a predefined classification, comprising the following steps: • the input image is processed to form an intermediate product by the image classifier using one or more convolutional layers; • the intermediate product is mapped onto a classification score with respect to at least one target class by the image classifier; • a disturbance in the space of the intermediate products is determined from one or more counter-images (5a-5c) which are assigned with priority to at least one class other than the target class by the image classifier, wherein the disturbance is formed from at least one intermediate product resulting from the processing of one or more counter-images (5a-5c) by the image classifier, wherein, from a plurality of counter-images, at least one counter-image is selected for which the intermediate product formed by the image classifier comes closest to the intermediate product formed from the input image according to a predefined distance measure, wherein a cosine distance between vectors containing the pixel values of the respective intermediate products is used as distance measure; • at least one binary mask is provided which has the same number of pixels as the intermediate product; • from the intermediate product, at least one modification (3*) is created in which pixels defined by the binary mask are replaced with pixels of the disturbance that correspond thereto, wherein a decision-relevant portion of the intermediate product determined from one or more binary masks is converted into the sought decision-relevant portion of the input image by interpolation or other upsampling, the one modification (3*) x' L being written with the binary mask m and a disturbance P L (x) defined on the basis of the input image x as: x L ′ = f L x ⊙ m + P L x ⊙ 1 − m, where x is the input image, and f L (x) is an intermediate product generated therefrom by the image classifier in the latent space; • the modification (3*) is mapped onto a classification score (7*) with respect to a predefined class by the image classifier; • from this classification score (7*), a quality function is used to determine to what extent the binary mask indicates the sought decision-relevant portions of the input image, wherein a quality assessment R x,f (m) of each mask m corresponds to a classification score f c assigned to this modification x' L by the image classifier: R x, f m = f c x L ′, wherein if N binary masks m i are drawn in the space of the intermediate products, an assessment number S(λ) will be written for example as: S x, f λ ≈ 1 E m ⋅ N ∑ i = 1 N R x, f m i ⋅ m i λ., where index f denotes the image classifier. • the input image is processed to form an intermediate product by the image classifier using one or more convolutional layers; • the intermediate product is mapped onto a classification score with respect to at least one target class by the image classifier; • a disturbance in the space of the intermediate products is determined from one or more counter-images (5a-5c) which are assigned with priority to at least one class other than the target class by the image classifier, wherein the disturbance is formed from at least one intermediate product resulting from the processing of one or more counter-images (5a-5c) by the image classifier, wherein, from a plurality of counter-images, at least one counter-image is selected for which the intermediate product formed by the image classifier comes closest to the intermediate product formed from the input image according to a predefined distance measure, wherein a cosine distance between vectors containing the pixel values of the respective intermediate products is used as distance measure; wherein the disturbance is formed from at least one intermediate product resulting from the processing of one or more counter-images (5a-5c) by the image classifier, wherein, from a plurality of counter-images, at least one counter-image is selected for which the intermediate product formed by the image classifier comes closest to the intermediate product formed from the input image according to a predefined distance measure, wherein a cosine distance between vectors containing the pixel values of the respective intermediate products is used as distance measure; • at least one binary mask is provided which has the same number of pixels as the intermediate product; • from the intermediate product, at least one modification (3*) is created in which pixels defined by the binary mask are replaced with pixels of the disturbance that correspond thereto, wherein a decision-relevant portion of the intermediate product determined from one or more binary masks is converted into the sought decision-relevant portion of the input image by interpolation or other upsampling, the one modification (3*) x' L being written with the binary mask m and a disturbance P L (x) defined on the basis of the input image x as: x L ′ = f L x ⊙ m + P L x ⊙ 1 − m, where x is the input image, and f L (x) is an intermediate product generated therefrom by the image classifier in the latent space; • the modification (3*) is mapped onto a classification score (7*) with respect to a predefined class by the image classifier; • from this classification score (7*), a quality function is used to determine to what extent the binary mask indicates the sought decision-relevant portions of the input image,