Patent application EP20166378A (EP), filed 2020-03-27
Publication number: EP3751453A1
Title: DETECTING ADVERSARIAL SAMPLES BY A VISION BASED PERCEPTION SYSTEM
Abstract: In one embodiment, a system receives a first image captured by a capturing device of an ADV. The system applies an image transformation to the first image to generate a second image. The system applies an object detection model to the first and the second images to generate a first and a second output, respectively. The system calculates a similarity metric between the first and the second output. The system detects the first image as an adversarial sample if a temporal variation of the similarity metric between the first image and a prior image is above a threshold.
IPC codes: G06K   9/    00            A I
G06K   9/    68            A I
G06F  18/    22            A I
G06V  10/    25            A I
G06V  10/    74            A I
G06V  20/    56            A I
Claims: number: 1
text: A computer-implemented method, comprising:
number: 2
text: The method of claim 1, wherein each of the first output and the second output includes a list of bounding boxes, the locations of the bounding boxes, and annotation of each class objects for the bounding boxes for the input image.
number: 3
text: The method of claim 1 or 2, wherein the image transformation includes a color depth reduction, an image compression, or a blurring transformation.
number: 4
text: The method of one of claims 1 to 3, wherein the similarity metric is calculated based on a distance between a plurality of inputs.
number: 5
text: The method of claim 4, wherein the distance includes differences in class prediction, number of bounding boxes, and overlapping regions of the bounding boxes.
number: 6
text: The method of one of claims 1 to 5, further comprising activating a failsafe mechanism for the ADV if an adversarial sample is detected.
number: 7
text: The method of claim 6, wherein the failsafe mechanism includes ignoring the adversarial sample or transferring control to a user of the ADV if the ADV is in self-driving mode.
number: 8
text: A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations, the operations comprising:
number: 9
text: The non-transitory machine-readable medium of claim 8, wherein each of the first output and the second output includes a list of bounding boxes, the locations of the bounding boxes, and annotation of each class objects for the bounding boxes for the input image.
number: 10
text: The non-transitory machine-readable medium of claim 8 or 9, wherein the image transformation includes a color depth reduction, an image compression, or a blurring transformation.
number: 11
text: The non-transitory machine-readable medium of one of claims 8 to 10, wherein the similarity metric is calculated based on a distance between a plurality of inputs.
number: 12
text: The non-transitory machine-readable medium of claim 11, wherein the distance includes differences in class prediction, number of bounding boxes, and overlapping regions of the bounding boxes.
number: 13
text: The non-transitory machine-readable medium of one of claims 8 to 12, wherein the operations further comprise activating a failsafe mechanism for the ADV if an adversarial sample is identified.
number: 14
text: The non-transitory machine-readable medium of claim 13, wherein the failsafe mechanism includes ignoring the adversarial sample or transferring control to a user of the ADV if the ADV is in self-driving mode.
number: 15
text: A data processing system (1500), comprising:
Office action: search_report, dated 2020-12-16
Prior art cited: ref_id: US2016205395A1
ref_type: patent
severity: background
ref_id: - KAI KANG ET AL: "T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 9 April 2016 (2016-04-09), XP081363902, DOI: 10.1109/TCSVT.2017.2736553
ref_type: npl
severity: background
ref_id: US2016205395
ref_type: patent
severity: background
ref_id: - KAI KANG ET AL, "T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20160409), doi:10.1109/TCSVT.2017.2736553, XP081363902 [I] 1-4,6-11,13-15 * III. Methods * [A] 5,12
ref_type: npl
severity: background
Final disposition: pending (2020-12-16)