Detection of an adversarial backdoor attack on a trained model at inference time

A computer-implemented method, comprising:

• recording, by a system operatively coupled to a processor, predictions and corresponding activation values generated by a trained model based on inference requests;
• and
• employing, by the system, a model at an inference time to detect a backdoor trigger request based on the predictions and the corresponding activation values, wherein the model is selected from a group consisting of a clustering model, an activation clustering model, a heuristic model, an outlier detector model, a trained outlier detector model, a local outlier factor model, a trained local outlier factor model, a novelty detector model, and a trained one class support vector machine model.

A computer program product facilitating detection of an adversarial backdoor attack on a trained model at inference time, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• record, by the processor, predictions and corresponding activation values generated by a trained model based on inference requests;
• and
• employ, by the processor, a model at an inference time to detect a backdoor trigger request based on the predictions and the corresponding activation values, wherein the model is selected from a group consisting of a clustering model, an activation clustering model, a heuristic model, an outlier detector model, a trained outlier detector model, a local outlier factor model, a trained local outlier factor model, a novelty detector model, and a trained one class support vector machine model.