US 12,141,704 B2
Neural flow attestation
Zhongshu Gu, Ridgewood, NJ (US); Xiaokui Shu, Ossining, NY (US); Hani Jamjoom, Cos Cob, CT (US); and Tengfei Ma, White Plains, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Aug. 22, 2023, as Appl. No. 18/236,590.
Application 18/236,590 is a continuation of application No. 16/750,328, filed on Jan. 23, 2020, granted, now 11,783,201.
Prior Publication US 2023/0394324 A1, Dec. 7, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/10 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/10 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method, in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor to configure the at least one processor to implement a neural flow attestation engine, the method comprising:
inputting, by the neural flow attestation engine, input data to a trained computer model, wherein the trained computer model comprises a plurality of layers of neurons;
recording, by the neural flow attestation engine, for a set of input data instances in the input data processed by the trained computer model, a neural flow through the plurality of layers of neurons, to thereby generate recorded neural flows;
deploying the trained computer model to a computing platform; and
verifying, by the neural flow attestation engine, an integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.