Jianyu J Wang

US Patent:

20200380300, Dec 3, 2020

Filed:

May 30, 2019

Appl. No.:

16/427233

Inventors:

- Sunnyvale CA, US
Jianyu WANG - Santa Clara CA, US

Assignee:

Baidu USA LLC - Sunnyvale CA

International Classification:

G06K 9/62
G06K 9/00

Abstract:

Described herein are embodiments for an approach to improve the robustness of an object detector against adversarial attacks. Existing attacks for object detectors and the impacts of individual task component on model robustness are systematically analyzed from a multi-task view of object detection. In one or more embodiments, a multi-task learning perspective of object detection is introduced and an asymmetric role of task losses is identified. One or more embodiments of an adversarial training method for robust object detection are presented to leverage the multiple sources of attacks for improving the robustness of object detection models.

US Patent:

20200265271, Aug 20, 2020

Filed:

Apr 30, 2019

Appl. No.:

16/399078

Inventors:

- Sunnyvale CA, US
Jianyu WANG - Santa Clara CA, US

Assignee:

Baidu USA LLC - Sunnyvale CA

International Classification:

G06K 9/62
G06N 20/00

Abstract:

Described herein are embodiments for joint adversarial training methods that incorporate both spatial transformation-based and pixel-value based attacks for improving image model robustness. Embodiments of a spatial transformation-based attack with an explicit notion of budgets are disclosed and embodiments of a practical methodology for efficient spatial attack generation are also disclosed. Furthermore, both pixel and spatial attacks are integrated into embodiments of a generation model and the complementary strengths of each other are leveraged for improving the overall model robustness. Extensive experimental results on several benchmark datasets compared with state-of-the-art methods verified the effectiveness of the presented method.