Abstract: Methods, apparatus, and processor-readable storage media for security-related image processing using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data associated with at least one user-provided component; obtaining identifier data associated with the at least one user-provided component; obtaining image data associated with at least one reference component from at least one database using at least a portion of the obtained identifier data; performing a comparison, using at least one pretrained computer vision model, of at least a portion of the obtained image data associated with the at least one user-provided component and at least a portion of the obtained image data associated with the at least one reference component; and performing one or more security-related actions based at least in part on results of the comparison.

Abstract: An apparatus comprises a processing device configured to obtain a plurality of images each comprising at least a given portion of an instance of a computing device and to detect, utilizing an image classification machine learning model, at least a subset of the plurality of images having one or more designated failure characteristics. The processing device is also configured to create, utilizing a semantic segmentation machine learning model, a mask for each image in the subset of the plurality of images, the created masks characterizing locations of the designated failure characteristics in each image in the subset of the plurality of images. The processing device is further configured to generate an aggregated view of locations of the designated failure characteristics for the subset of the plurality of images utilizing the created masks, and to perform failure analysis for the computing device utilizing the generated aggregated view.

Abstract: Methods, apparatus, and processor-readable storage media for security-related image processing using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data associated with at least one user-provided component; obtaining identifier data associated with the at least one user-provided component; obtaining image data associated with at least one reference component from at least one database using at least a portion of the obtained identifier data; performing a comparison, using at least one pretrained computer vision model, of at least a portion of the obtained image data associated with the at least one user-provided component and at least a portion of the obtained image data associated with the at least one reference component; and performing one or more security-related actions based at least in part on results of the comparison.

Abstract: An apparatus comprises a processing device configured to obtain a first image, to detect one or more designated types of objects in the first image, at least a given one of the designated types of objects comprising at least a given portion of a computing device associated with a service request and, responsive to detecting the given one of the designated types of objects, to identify features of the given portion of the computing device in the first image. The processing device is also configured to perform an augmentation of the identified features of the given portion of the computing device to generate a second image, the second image comprising a modified version of the first image that contains the given portion of the computing device and excludes one or more other portions of the first image, and to process the service request utilizing the second image.

Abstract: An apparatus comprises a processing device configured to obtain a plurality of images each comprising at least a given portion of an instance of a computing device and to detect, utilizing an image classification machine learning model, at least a subset of the plurality of images having one or more designated failure characteristics. The processing device is also configured to create, utilizing a semantic segmentation machine learning model, a mask for each image in the subset of the plurality of images, the created masks characterizing locations of the designated failure characteristics in each image in the subset of the plurality of images. The processing device is further configured to generate an aggregated view of locations of the designated failure characteristics for the subset of the plurality of images utilizing the created masks, and to perform failure analysis for the computing device utilizing the generated aggregated view.

Abstract: An apparatus comprises a processing device configured to obtain a first image, to detect one or more designated types of objects in the first image, at least a given one of the designated types of objects comprising at least a given portion of a computing device associated with a service request and, responsive to detecting the given one of the designated types of objects, to identify features of the given portion of the computing device in the first image. The processing device is also configured to perform an augmentation of the identified features of the given portion of the computing device to generate a second image, the second image comprising a modified version of the first image that contains the given portion of the computing device and excludes one or more other portions of the first image, and to process the service request utilizing the second image.