Invention Grant
- Patent Title: Identifying artificial artifacts in input data to detect adversarial attacks
-
Application No.: US15885935Application Date: 2018-02-01
-
Publication No.: US10944767B2Publication Date: 2021-03-09
- Inventor: Gaurav Goswami , Sharathchandra Pankanti , Nalini K. Ratha , Richa Singh , Mayank Vatsa
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Stephen J. Walder, Jr.; Jeffrey S. LaBaw
- Main IPC: G06F11/00
- IPC: G06F11/00 ; G06F12/14 ; G06F12/16 ; G08B23/00 ; H04L29/06 ; G06N3/08 ; G06N3/04 ; G06F21/56

Abstract:
Mechanisms are provided for training a classifier to identify adversarial input data. A neural network processes original input data representing a plurality of non-adversarial original input data and mean output learning logic determines a mean response for each intermediate layer of the neural network based on results of processing the original input data. The neural network processes adversarial input data and layer-wise comparison logic compares, for each intermediate layer of the neural network, a response generated by the intermediate layer based on processing the adversarial input data, to the mean response associated with the intermediate layer, to thereby generate a distance metric for the intermediate layer. The layer-wise comparison logic generates a vector output based on the distance metrics that is used to train a classifier to identify adversarial input data based on responses generated by intermediate layers of the neural network.
Public/Granted literature
- US20190238568A1 Identifying Artificial Artifacts in Input Data to Detect Adversarial Attacks Public/Granted day:2019-08-01
Information query