Synthetic Content: Exploring the Risks, Technical Approaches, and Regulatory Responses is a 35-page report published by the Future of Privacy Forum (FPF) in October 2024 and authored by Jameson Spivack, Senior Policy Analyst. It surveys AI-generated synthetic content — text, images, video, and audio increasingly indistinguishable from authentic content — across three dimensions: categories of risk, technical and organizational mitigation approaches, and the privacy and security tradeoffs each mitigation introduces. An appendix surveys the US legislative and regulatory landscape as of October 2024.
| Publisher | Future of Privacy Forum (FPF) |
| Author | Jameson Spivack, Senior Policy Analyst |
| Published | October 2024 |
| Pages | 35 |
Summary
The report organizes its analysis around four elements: seven categories of risk from synthetic content; seven technical and organizational mitigation approaches; the privacy and security tradeoffs of each mitigation; and an appendix on the US legislative and regulatory landscape.
Risk categories
The report identifies seven categories of risk.
| Category | Examples | Severity |
|---|---|---|
| Malicious impersonation | Voice cloning scams, executive fraud | High; direct financial/reputational harm |
| Disinformation/misinformation | Deepfake political videos, AI-generated news | High; societal; nearly impossible to fully detect |
| Synthetic NCII | AI-generated non-consensual intimate imagery | High; personal harm; subject of TAKE IT DOWN Act |
| Synthetic CSAM | AI-generated child sexual abuse material | Severe; legal prohibition in most jurisdictions |
| Financial scams | Synthetic CEO audio for wire fraud | High; significant losses documented |
| Discrimination | Synthetic content used for discriminatory decisions | Medium; intersects with existing anti-discrimination law |
| Loss of trust in media | "Liar's dividend" — authentic content dismissed as fake | Long-term societal harm |
The "loss of trust in media" category is framed around the "liar's dividend," in which the existence of convincing synthetic content lets authentic content be dismissed as fake (Liar's Dividend).
Technical mitigation approaches
The report surveys seven technical and organizational mitigation approaches, each with stated strengths and limitations.
| Approach | Mechanism | Strengths | Limitations |
|---|---|---|---|
| Watermarking | Embed invisible signal in AI-generated output | Scalable; real-time | Easily stripped; not universal |
| Provenance tracking | C2PA-style content credentials attached to file | Interoperable; chain of custody | Requires adoption throughout distribution chain |
| Metadata recording | Store generation parameters in file metadata | Lightweight | Easily stripped; not authenticated |
| Labeling/disclosure | Require disclosure of AI origin to users | Consumer-facing | Self-attestation model; often missed |
| Synthetic content detection | AI classifiers detecting AI-generated content | Scalable | High false positive/negative rates; arms race with generators |
| Hashing/filtering | Match known-harmful content hashes (PhotoDNA model) | Effective for known CSAM/NCII | Doesn't work for novel content |
| Legal prohibitions | Criminal/civil law against deepfake impersonation | Deterrence | Extraterritorial enforcement; victim identification |
C2PA-style provenance tracking is one of the seven approaches (Data Provenance, C2PA, and Watermarking).
Privacy and security tradeoffs
The report argues that each mitigation approach creates its own privacy and security risks:
- Provenance tracking (C2PA) requires authentication infrastructure that, if compromised, creates new surveillance risks.
- Content detection at platform scale creates a surveillance layer that could be repurposed.
- Watermarking metadata can reveal creation tools, potentially identifying sources.
- Hashing requires hash databases that, if breached, expose sensitive content.
The report concludes that no single approach is sufficient and all involve tradeoffs, and that the optimal strategy combines multiple approaches while maintaining explicit safeguards for personal data in detection and authentication systems.
US regulatory landscape (as of October 2024)
At the federal level, the report records no comprehensive federal deepfake law as of its writing. It notes that the FTC, CFPB, FCC, and SEC have authority over specific harms (fraud, impersonation, robocalls). The bipartisan Senate AI Working Group Roadmap (2024) identified synthetic content as a priority, and the federal NO FAKES Act, addressing voice/likeness replica protection, was proposed but not enacted as of October 2024.
At the state level, the report documents NCII and synthetic CSAM laws in most states, political deepfake laws in Minnesota, Washington, Texas, and California (State Deepfake Statutes (MN, WA, TX, CA)), and California's AI Transparency Act (California AI Transparency Act (SB 942)), which covers AI-generated content labeling.
After the report's publication, the TAKE IT DOWN Act (P.L. 119-12, May 2025) enacted federal NCII/deepfake criminal penalties and a 48-hour takedown requirement (TAKE IT DOWN Act — Source Summary), partially addressing the federal gap the report identified.
Provenance and confidence
FPF is a recognized non-profit, and the document is a substantive research report rather than an opinion piece, supporting a high confidence rating. The October 2024 date means the US regulatory landscape has since changed, notably with the enactment of the TAKE IT DOWN Act, so claims about current law should be read as of October 2024. The report provides a taxonomy of synthetic content risks, documents the seven mitigation techniques with tradeoff analysis, explains the privacy-security tension in detection and watermarking approaches, and supplies a regulatory baseline against which the TAKE IT DOWN Act can be understood. It serves as a technical and regulatory survey supporting Synthetic Media / Deepfakes.
Relationships
- supports: Synthetic Media / Deepfakes — technical and regulatory survey of synthetic content.
- supports: Liar's Dividend — the "loss of trust in media" risk category corresponds to the liar's dividend formulation.
- related: TAKE IT DOWN Act — Source Summary — federal law that partially addressed the gap identified here.
- related: State Deepfake Statutes (MN, WA, TX, CA) — state legislative landscape documented in this report.
- related: Data Provenance, C2PA, and Watermarking — C2PA is one of the seven technical approaches.
- related: AI in Elections and Democratic Institutions — synthetic media's election-specific implications.
- related: Incentives or Obligations? The U.S. Regulatory Approach to Voluntary AI Governance Standards — same FPF author (Spivack); companion on voluntary standards becoming binding.
- related: The Price is Right: Responsible Uses of Personal Data in Pricing — same FPF author; companion on responsible data use.
- related: Future of Privacy Forum (FPF) — publisher.
- related: The Digitalist Papers (Stanford, Volumes 1–2) — Persily's "Misunderstanding AI's Democracy Problem" provides the canonical articulation of the liar's-dividend thesis the FPF report documents empirically.