Invention Grant
- Patent Title: Quantifying vulnerabilities of deep learning computing systems to adversarial perturbations
-
Application No.: US16296897Application Date: 2019-03-08
-
Publication No.: US11227215B2Publication Date: 2022-01-18
- Inventor: Sijia Liu , Quanfu Fan , Chuang Gan , Dakuo Wang
- 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: G06N3/08
- IPC: G06N3/08 ; G06N20/00

Abstract:
Mechanisms are provided for generating an adversarial perturbation attack sensitivity (APAS) visualization. The mechanisms receive a natural input dataset and a corresponding adversarial attack input dataset, where the adversarial attack input dataset comprises perturbations intended to cause a misclassification by a computer model. The mechanisms determine a sensitivity measure of the computer model to the perturbations in the adversarial attack input dataset based on a processing of the natural input dataset and corresponding adversarial attack input dataset by the computer model. The mechanisms generate a classification activation map (CAM) for the computer model based on results of the processing and a sensitivity overlay based on the sensitivity measure. The sensitivity overlay graphically represents different classifications of perturbation sensitivities. The mechanisms apply the sensitivity overlay to the CAM to generate and output a graphical visualization output of the computer model sensitivity to perturbations of adversarial attacks.
Public/Granted literature
- US20200285952A1 Quantifying Vulnerabilities of Deep Learning Computing Systems to Adversarial Perturbations Public/Granted day:2020-09-10
Information query