The invention provides a frame-level alignment boundary confrontation
attack method,
system and device for a sequence recognition model, equipment and a medium, and belongs to the field of
computer vision, voice
processing and confrontation
machine learning. The method comprises the following steps: S1, inputting a sample into a target sequence identification model in a test stage to obtain an initial
reference alignment tag sequence; s2, based on the initial
reference alignment tag sequence, constructing an alignment boundary between the
reference alignment tag and the competition tag; s3, on the basis of the aligned boundary margins, dynamic continuous gating weights are generated through smooth mapping, a gating weighted marginal optimization target is constructed and iteratively updated, and candidate adversarial samples are obtained; and S4, performing total variation (TV)
smoothing and
amplitude scaling search on the candidate adversarial samples under successful retention constraints to generate high-fidelity adversarial samples. The method can improve the
attack resisting efficiency and success rate, and can be used for scenes of robustness evaluation,
privacy protection, copyright protection and the like of a sequence recognition model.