Spatio-Temporal IoU Constraints Based Adversarial Defense Method for Object Tracking
With the wide application of deep learning in the field of visual tracking,adversarial attack is one of key factors affecting the model performance.However,the research on defense methods for adversarial attack is still in the initial stage.Therefore,a spatio-temporal intersection over union(IoU)constraints based adversarial defense method for object tracking is proposed.In this method,Gaussian noise constraints are firstly added to the adversarial examples.Then,according to the tangent direction of the noise contour,the tangential constraint with the same noise level and the highest spatio-temporal IoU score is selected.The normal constraint is utilized to update the defense target towards the direction of the original image,and the normal and tangential constraints are orthogonally combined and optimized.Finally,the combined vector with the highest spatio-temporal IoU score and the lowest noise level is selected as the best constraint,and it is added to the adversarial example image and transferred to the next frame image,thereby realizing temporal defense.Experiments on VOT2018,OTB100,GOT-10k and LaSOT tracking datasets verify the validity of the proposed method.
Object TrackingAdversarial DefenseAdversarial AttackSpatio-Temporal Intersection over Union
NETL
NSTL
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