Doctor Trigger: A Framework for Release-Bound Face Verification
cs.CR
Submitted: 2026-10-12
Updated: 2026-10-14
Comments: View our project page: https://ming053l.github.io/Doctor-Trigger/
Project page: https://ming053l.github.io/Doctor-Trigger
License: http://creativecommons.org/licenses/by/4.0/
The gist: Face swapping and generative editing enable realistic facial manipulation, while online redistribution complicates publisher oversight of circulating face images.
Terminology
Abstract
Face swapping and generative editing enable realistic facial manipulation, while online redistribution complicates publisher oversight of circulating face images. Existing approaches establish provenance, detect manipulation, recover embedded watermarks, verify image integrity, or disrupt unauthorized face manipulation. These capabilities do not necessarily establish consistency with a specific publisher-enrolled release, limiting the evidence available for verification and human review. To address this gap, we introduce Doctor Trigger, a framework for verifying circulating faces against private publisher-held release records. Before publication, the publisher embeds a weak keyed Fourier-phase signal in each face while preserving visual fidelity and stores its spectral reference. Afterward, verification compares the circulating face with that reference and uses key projection to measure retention of the release-specific signal. Therefore, relation and key evidence jointly help publishers identify suspicious copies for review, potentially reducing the circulation of manipulated images. Experimental results across FF++, DFDC, and Celeb-DF show that spectral agreement separates tested face swaps and generative manipulations from enrolled releases, while the keyed signal distinguishes published versions from their unreleased originals under the evaluated key-preserving benign transformations. Doctor Trigger complements provenance metadata with release-specific face verification and record-linked evidence for further assessment.
Sources
- The DeepFake Detection Challenge (DFDC) Dataset
- DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication
- Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach
- Interoperable Provenance Authentication of Broadcast Media using Open Standards-based Metadata, Watermarking and Cryptography
- FaceGuard: Proactive Deepfake Detection
- Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
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