ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2504.21846
36
0

Active Light Modulation to Counter Manipulation of Speech Visual Content

30 April 2025
Hadleigh Schwartz
Xiaofeng Yan
Charles J. Carver
Xia Zhou
ArXivPDFHTML
Abstract

High-profile speech videos are prime targets for falsification, owing to their accessibility and influence. This work proposes Spotlight, a low-overhead and unobtrusive system for protecting live speech videos from visual falsification of speaker identity and lip and facial motion. Unlike predominant falsification detection methods operating in the digital domain, Spotlight creates dynamic physical signatures at the event site and embeds them into all video recordings via imperceptible modulated light. These physical signatures encode semantically-meaningful features unique to the speech event, including the speaker's identity and facial motion, and are cryptographically-secured to prevent spoofing. The signatures can be extracted from any video downstream and validated against the portrayed speech content to check its integrity. Key elements of Spotlight include (1) a framework for generating extremely compact (i.e., 150-bit), pose-invariant speech video features, based on locality-sensitive hashing; and (2) an optical modulation scheme that embeds >200 bps into video while remaining imperceptible both in video and live. Prototype experiments on extensive video datasets show Spotlight achieves AUCs ≥\geq≥ 0.99 and an overall true positive rate of 100% in detecting falsified videos. Further, Spotlight is highly robust across recording conditions, video post-processing techniques, and white-box adversarial attacks on its video feature extraction methodologies.

View on arXiv
@article{schwartz2025_2504.21846,
  title={ Active Light Modulation to Counter Manipulation of Speech Visual Content },
  author={ Hadleigh Schwartz and Xiaofeng Yan and Charles J. Carver and Xia Zhou },
  journal={arXiv preprint arXiv:2504.21846},
  year={ 2025 }
}
Comments on this paper