Video forensics method based on transformer and quantum characteristics

By adopting a multimodal video forensics method based on Transformer and quantum features, the robustness and inaccuracy of video forensics technology are solved, achieving efficient and accurate video tampering identification and location, and adapting to the video forensics needs of complex scenarios.

CN122027849BActive Publication Date: 2026-06-23CHENGDU UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202610478167.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-13
Publication Date
2026-06-23
Estimated Expiration
2046-04-13

AI Technical Summary

Technical Problem

Existing video forensics technologies lack robustness when facing complex tampering methods, making it difficult to achieve accuracy, positioning precision, and universality. They also lack multimodal feature optimization and closed-loop optimization mechanisms, leading to misjudgments and missed judgments.

Method used

A video forensics method based on Transformer and quantum features is adopted. Through multimodal data acquisition, preprocessing, single-modal preliminary detection, quantum-optimized feature fusion and closed-loop verification, combined with quantum surface fitting algorithm to optimize features, the efficient fusion and judgment of multimodal features are achieved.

Benefits of technology

It improves the robustness of video authenticity determination and the accuracy of locating tampered areas, breaks through the efficiency bottleneck of traditional feature optimization, adapts to the evidence collection needs of different scenarios, and outputs clear determination reports.

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Abstract

The application provides a video forensics method based on a Transformer and quantum characteristics, and belongs to the technical field of multimedia content security and computer vision, and the method comprises the following steps: S1, collecting multi-modal original data; S2, pre-processing multi-modal data; S3, preliminarily detecting single-modal authenticity; S4, quantum-optimized multi-modal feature fusion; and S5, comprehensively determining multi-modal authenticity. Through multi-modal cooperation and quantum technology innovation, the application effectively solves the problems of insufficient robustness and inaccurate positioning of traditional forensics technology, and provides an efficient, accurate and feasible technical solution for video content authenticity verification.
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Citation Information

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