A new Ofcom discussion paper highlights how platforms and users can identify deepfakes by using attribution measures—such as watermarks, provenance metadata, AI labels, and context annotations—to help trace the origins of synthetic content .
But how does this compare to forensic analysis?
The Attribution Approach: Inform and Empower
Ofcom emphasizes four main attribution measures:
Watermarking – Invisible embedding in media to verify authenticity
Provenance Metadata – Structured data indicating origin and creation chain
AI Labels – Visible tags that alert users to AI-generated content
Context Annotations – Descriptive notes on how and why content was created
However, they flag these key considerations when using attribution:
Attribution can help users engage critically, but isn’t simple enough for end-users alone.
Watermarks can be removed; metadata tampered with.
Context and labeling must handle mixed-content media flexibly.
Attribution should be paired with other interventions—such as automated detection
Forensic Analysis: Detecting Fakes, Watermark or Not
Forensic analysis builds on the multi-layered strategy Ofcom recommends—especially where attribution alone falls short.
Forensics Core Capabilities:
AI-Powered Media Forensics
AI to detects deepfake telltales: both visible anomalies and structural anomalies which may be indiscernable to the naked eye —identifying fakes even when no watermark is present.Metadata & Context Fusion
Like attribution systems, forensics read metadata—but going deeper by correlating it with device info, upload history, file signatures, and user behavior to spot when metadata is forged or mismatched.Playback Attack Detection
Detecting silent watermarking fails when a screen-recorded video is bypassed; forensic detection flags these by spotting telltales of recorded playback, such as reflection patterns or framerate inconsistencies.Explainable Forensics & Human-In-The-Loop
Clear forensic evidence— not “probably fake,” but “AI‑driven dissonance in frame 42”—which enables reviewers or legal teams to make confident decisions.
Now that’s not to say attribution does not have a place.
Attribution + Forensics = Comprehensive Defense
| Situation | Attribution Systems | Attestiv Forensics |
|---|---|---|
| AI‑generated with watermark |  Proven provenance flagged |  Forensic validated |
| Watermark-stripped or metadata-faked |  Attribution lost |  Forensic detection still works |
| Replayed deepfake video |  Attribution buried |  Playback artifacts found |
| User submits mixed content |  Labels may not reflect partial authenticity |  Granular frame-level analysis |
| Platforms need compliance log |  Attribution provides baseline evidence |  Forensic reports give detailed audit trails |
Why Platforms and Enterprises Need Both
User Trust & Transparency: Attribution ensures users know content might not be real.
Robust Security & Compliance: Forensic analysis catches fakes bypassing attribution and supports legal cases.
Scalability Meets Accuracy: Attribution systems help at scale; forensic systems ensure depth and rigor.
Final Take
Ofcom’s push for watermarks, metadata, labels, and context is a vital step forward. But attribution alone isn’t enough—malicious content can be stripped, faked, or reborn via screen recorders.
Attestiv offers the forensic layer that fills this gap—detecting deepfakes at scale, even when provenance signals are gone.
In an era of synthetic trust erosion, the solution isn’t binary—it’s layered.
Explore how Attestiv integrates forensic and attribution-based deepfake protection for platforms and enterprises:
Learn more about Attestiv’s Cybersecurity Deepfake Detection
