A computing-device implemented method for validating a cargo manifest associated with a shipping container, the method comprising: receiving, at a computing device, a rendered radiographic image of a scanned shipping container from a scanner configured to scan the shipping container and render the radiographic image; extracting, with an autoencoder neural network, a feature vector from the radiographic image; determining a statistical probability that an object in the scanned shipping container matches a listed object on a cargo manifest associated with the scanned shipping container based at least in part on: a comparison of the extracted feature vector against a historic distribution of prior feature vectors of radiographic images of prior shipping containers associated with cargo manifests containing a like object; and generating an automated detection indication related to the scanned shipping container based on the determined statistical probability, the automated detection indication: providing a graphical indicator of a degree of consistency of the content of the scanned container with the associated cargo manifest, and/or providing a display of a ranking of radiographic images, and/or being a graphical indication or an audible indication alerting a user that an anomaly is detected in the radiographic image, and/or being a text label identifying the anomaly, and/or being a colored box shape enclosing the anomaly, and/or indicating whether the radiographic image is consistent with an empty container; characterized in that the statistical probability is further determined based at least in part on natural language processing of the cargo manifest by a second artificial neural network. receiving, at a computing device, a rendered radiographic image of a scanned shipping container from a scanner configured to scan the shipping container and render the radiographic image; extracting, with an autoencoder neural network, a feature vector from the radiographic image; determining a statistical probability that an object in the scanned shipping container matches a listed object on a cargo manifest associated with the scanned shipping container based at least in part on: a comparison of the extracted feature vector against a historic distribution of prior feature vectors of radiographic images of prior shipping containers associated with cargo manifests containing a like object; and generating an automated detection indication related to the scanned shipping container based on the determined statistical probability, the automated detection indication: providing a graphical indicator of a degree of consistency of the content of the scanned container with the associated cargo manifest, and/or providing a display of a ranking of radiographic images, and/or being a graphical indication or an audible indication alerting a user that an anomaly is detected in the radiographic image, and/or being a text label identifying the anomaly, and/or being a colored box shape enclosing the anomaly, and/or indicating whether the radiographic image is consistent with an empty container; providing a graphical indicator of a degree of consistency of the content of the scanned container with the associated cargo manifest, and/or providing a display of a ranking of radiographic images, and/or being a graphical indication or an audible indication alerting a user that an anomaly is detected in the radiographic image, and/or being a text label identifying the anomaly, and/or being a colored box shape enclosing the anomaly, and/or indicating whether the radiographic image is consistent with an empty container; characterized in that the statistical probability is further determined based at least in part on natural language processing of the cargo manifest by a second artificial neural network.