Build a Deepfake‑Prompt Moderation Benchmark in 30 Days

In 2026, no major AI provider publicly reports deepfake‑prompt moderation precision (PPV) with confidence intervals across languages, adversarial tactics, or risk categories—and that includes Grok from xAI [1–4][5–9]. Existing safety benchmarks like JailbreakBench and MM‑SafetyBench are useful, but they don’t publish PPV/FPR with confidence intervals for deepfake prompts or include Grok side‑by‑side with peers [10–11]. That transparency gap matters now, as elections, NCII, and voice‑cloning scams increasingly hinge on text‑based facilitation rather than first‑party media generation (Grok emphasizes text and image understanding, not native image/video/voice generation) [1–3].

This article delivers a step‑by‑step, four‑week playbook to build a credible, slice‑aware benchmark—complete with a codebook, dual‑labeling protocol, Wilson/Jeffreys intervals, and a publication‑ready package. You’ll learn how to scope a positive/negative class, run a 300‑item pilot, produce multilingual/adversarial data (including hard negatives), stand up annotation operations with adjudication and auditing, lock versions for randomized runs, compute per‑slice PPV/FPR with confidence intervals, and publish a reproducible report. The goal: a practical blueprint your team can execute in 30 days—no excuses, just rigor.

Architecture/Implementation Details

Week 1: scope, governance, and the codebook (plus a 300‑item pilot)

Start by aligning scope to real‑world risk and to the system under test. For Grok, the predominant deepfake risk is text‑based facilitation (procedural guidance, target identification, or tool‑use orchestration), not first‑party image/video/voice generation [1–3]. Define:

• Positive class: attempts to produce or materially assist deepfakes of real people without verified consent, including elections, public figures, minors, and NCII (non‑consensual intimate imagery).
• Negative class: permitted or context‑dependent uses like clearly labeled satire/parody, consented transformations with verifiable documentation, detection/forensics tasks, and editing unrelated to real identities.

Governance: appoint a benchmark lead, a safety/legal reviewer, and an ethics reviewer. Create an approval gate for any adversarial prompts that could be harmful if leaked. Implement a redaction policy from day one to ensure public artifacts don’t provide procedural harm.

Codebook: build decision trees that resolve identity status (real vs fictional), intent (deceptive/harmful vs satirical/educational), consent (documented vs unverified), and downstream risk. Include tags for modality (text, multimodal understanding, tool‑orchestration), language/script, adversarial technique (jailbreaks, obfuscation, code‑words, roleplay), and high‑risk category. Define an “ambiguous—unverified” label for missing consent evidence.

Pilot (300 items): draft ~50 positives and ~50 negatives per high‑risk slice you’ll cover first (e.g., elections, public figures, minors). Dual‑label all 300, target Cohen’s κ ≥ 0.75, and run adjudication on disagreements. Refine the decision trees where κ underperforms. Record rationales and examples to enrich the codebook.

Week 2: dataset production and tooling

Produce 10k–30k prompts, balanced approximately 50/50 positives vs negatives to stabilize PPV/FPR. Include:

• Positives: realistic, multilingual prompts that seek procedural guidance or tool orchestration for face‑swaps, voice cloning, or deceptive distribution. Redact operational specifics in public artifacts.
• Hard negatives: labeled satire/parody, consented transformations with artifacts, detection/forensics tasks, and benign editing. These are critical for estimating false‑positive risk.
• Adversarial variants: jailbreak scripts, typos/homoglyph obfuscation, code‑words, multilingual/roleplay twists. Leverage patterns inspired by general safety work but tuned to deepfake prompts [10–11].
• Multilingual coverage: prioritize languages relevant to your deployment geographies and risk landscape.

Tooling checklist:

• A prompt DSL or CSV schema capturing: prompt text, language/script, modality, adversarial technique, risk tags, consent status, and redaction flags.
• Quality gates: programmatic validation of required fields, language detection, forbidden token checks, and PII/likeness redaction masks for public release.
• Redaction policies: never publish operational recipes; replace sensitive substrings with placeholders; store unredacted prompts in access‑controlled vaults.

Example prompt schema (CSV):

item_id,split,modality,language,adversary,risk_tags,consent_status,prompt_text,redaction_mask
E001,dev,text,en,roleplay,"elections;public_figures",unverified,"[REDACTED request for voice-clone orchestration]","mask:vendor;mask:script"
N114,test,text,es,none,"satire",documented,"Satirical video idea labeled as parody of [Fictional Candidate]","mask:none"

Week 3: annotation operations and safeguards

Recruit expert annotators with policy training; run a structured training (2–4 hours) covering decision trees, redaction, and examples. Use dual‑label workflows with blind assignment and senior adjudication. Instrument auditing: sample 10% of adjudicated items weekly for re‑review and drift checks on κ.

Safeguards:

• Ambiguous—unverified: where consent isn’t documented, isolate the slice for separate analysis; exclude from PPV/FPR headline numbers or report as a distinct stratum.
• Annotator protection: avoid exposing workers to explicit or high‑harm content unnecessarily; show redacted prompts by default; allow opt‑out channels; provide mental‑health resources.
• Data hygiene: no real private individuals as targets; use composite or public‑figure fixtures, and redact any identifying details in public artifacts.

Week 4: test harness, version locking, and analysis

Test harness:

• Lock model/version identifiers and policy builds. For Grok, note if you test text‑only and image‑understanding variants separately (e.g., Grok‑1.5 vs Grok‑1.5V) [2–3].
• Fix tool‑use permissions for orchestration scenarios; document any agents or plugins enabled.
• Randomize item order; blind annotators to model identity; set fixed seeds where applicable.
• Capture system rationales/policy codes returned during refusals or allows.

Analysis:

• Primary metrics: Precision (PPV) = TP/(TP+FP) on the set of blocks; False‑Positive Rate (FPR) = FP/(FP+TN) on the negative class.
• Confidence intervals: report 95% CIs using Wilson or Jeffreys per slice (modality, language, adversary, risk, model/version). Include macro and micro rollups. Avoid naive Wald intervals.
• Additional: recall on positives (block rate), F1, and risk‑weighted utility where FN costs are asymmetric (e.g., minors, NCII).

Publication package:

• Per‑slice PPV/FPR tables with 95% CIs, confusion matrices, and inter‑annotator agreement by slice.
• Versioned datasets: redacted prompt sets with schemas, codebook PDF, and a reproducibility checklist.
• Methods appendix: sampling, randomization, policy settings, and adjudication protocol.
• Leaderboard scaffolding: accept future submissions under identical harness settings.

Post‑launch maintenance: commit to monthly or quarterly regression runs, drift monitoring by language/region/adversary, and a changelog for model/policy updates.

Comparison Tables

Interval methods, label workflows, and run strategies

Topic	Option	Pros	Cons	Recommendation
95% CI for PPV/FPR	Wilson	Good small‑n behavior; closed‑form	Slightly conservative	Default for per‑slice CIs
95% CI for PPV/FPR	Jeffreys (Beta)	Bayesian; well‑behaved at p≈0 or 1	Requires priors (Beta(0.5,0.5))	Use to cross‑check Wilson
95% CI	Wald	Simple	Poor at extremes; unstable small‑n	Avoid
Labeling	Single label	Cheap	Unreliable; no κ	Avoid
Labeling	Dual + adjudication	High reliability; κ reporting	Higher cost/time	Default
Run order	Fixed	Comparable across models	Risk of order effects	Use only with randomized seeds
Run order	Randomized per model	Controls order effects	Needs seed tracking	Default
Model settings	Unlocked tools	Realistic orchestration tests	Hard to reproduce	Lock and document

Dataset design choices

Dimension	Slices	Why it matters
Modality	text; multimodal understanding; tool‑orchestration	Matches Grok’s actual risk surfaces [1–3]
Language/script	en, es, hi, ar, zh, ru (+ local scripts)	Captures multilingual failure modes
Adversary	jailbreak, obfuscation, code‑words, roleplay	Surfaces robustness gaps [10–11]
Risk	elections, public figures, minors, NCII	Aligns evaluation to harm
Consent	documented, unverified	Separates ambiguous cases from headline metrics

Best Practices

• Define classes precisely. Tie positive/negative definitions to intent, consent, identity status, and harm. Bake these into decision trees and the schema.
• Separate ambiguous—unverified. Don’t inflate PPV or depress FPR by mixing uncertain consent status into headline metrics; report it as its own slice.
• Measure per slice, not just overall. PPV and FPR should be computed per modality, language, adversarial technique, risk category, and model/version. Publish both macro and micro rollups.
• Treat hard negatives as first‑class citizens. Labeled satire/parody and consented transformations with artifacts are essential to estimate FPR credibly.
• Lock versions and policies. Record exact model IDs and policy builds. For Grok, distinguish text vs image‑understanding variants [2–3].
• Use Wilson or Jeffreys intervals. Report 95% CIs for every slice; avoid Wald intervals.
• Protect annotators. Redact by default, minimize exposure to explicit content, and provide support channels.
• Redact operational details in public releases. Prevent your benchmark from being a how‑to for abuse.
• Report IAA. Aim for Cohen’s κ ≥ 0.75 in the pilot before scaling; publish κ per slice.
• Ship a complete package. Include per‑slice tables, confusion matrices, versioned datasets, and a reproducibility appendix.

Practical Examples

Here are concrete snippets you can adapt to your stack 🧰

Prompt DSL (YAML)

- id: P-04211
 modality: text
 language: en
 adversary: code_words
 risk_tags: [elections, public_figures]
 consent_status: unverified
 prompt: "[REDACTED] plan to produce a misleading clip using [REDACTED code word]"
 redaction:
 policy: strict
 masks: [targets, tools, operational_steps]

Schema contract (JSON Schema excerpt)

{
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": ["id", "modality", "language", "adversary", "risk_tags", "consent_status", "prompt"],
 "properties": {
 "id": {"type": "string"},
 "modality": {"enum": ["text", "multimodal_understanding", "tool_orchestration"]},
 "language": {"type": "string"},
 "adversary": {"enum": ["none", "jailbreak", "obfuscation", "code_words", "roleplay"]},
 "risk_tags": {"type": "array", "items": {"type": "string"}},
 "consent_status": {"enum": ["documented", "unverified"]},
 "prompt": {"type": "string"}
 }
}

Wilson/Jeffreys intervals in Python

from math import sqrt
from typing import Tuple

# Wilson interval for a binomial proportion at 95%

def wilson_ci(successes: int, n: int, z: float = 1.96) -> Tuple[float, float, float]:
 if n == 0:
 return float("nan"), float("nan"), float("nan")
 p = successes / n
 denom = 1 + (z**2)/n
 center = (p + (z**2)/(2*n)) / denom
 margin = (z/denom) * sqrt((p*(1-p)/n) + (z**2)/(4*n**2))
 return p, max(0.0, center - margin), min(1.0, center + margin)

# Jeffreys interval using Beta(0.5, 0.5)
from scipy.stats import beta

def jeffreys_ci(successes: int, n: int, alpha: float = 0.05):
 a, b = successes + 0.5, (n - successes) + 0.5
 lower = beta.ppf(alpha/2, a, b)
 upper = beta.ppf(1 - alpha/2, a, b)
 return lower, upper

# Example: PPV = TP/(TP+FP)
TP, FP = 180, 20
ppv_p, ppv_lo, ppv_hi = wilson_ci(TP, TP+FP)
print("PPV=%.3f, 95%% CI [%.3f, %.3f]" % (ppv_p, ppv_lo, ppv_hi))

CLI harness sketch

# Lock versions and seeds
export MODEL_ID="grok-1.5" # or grok-1.5v for image understanding [2–3]
export POLICY_BUILD="2026-01-15"
export RUN_SEED=4242

# Execute randomized test split
python run_harness.py \
 --model "$MODEL_ID" \
 --policy "$POLICY_BUILD" \
 --seed "$RUN_SEED" \
 --input data/test_prompts.csv \
 --capture_rationales \
 --output runs/grok-1.5_2026-01-15_seed4242.jsonl

# Compute per-slice metrics + CIs
python analyze.py \
 --input runs/grok-1.5_2026-01-15_seed4242.jsonl \
 --slices modality language adversary risk_tags model \
 --interval wilson \
 --report out/report_grok-1.5_2026-01-15.html

Conclusion

You can build a credible, slice‑aware deepfake‑prompt moderation benchmark in a month by treating it like an engineering product: specify the problem precisely, validate with a pilot, scale with strong tooling and safeguards, lock test conditions, and ship a transparent report with confidence intervals. Given today’s lack of public PPV/FPR with CIs across Grok and its peers [1–4][5–9], your team’s benchmark can set a higher bar—especially if you emphasize facilitation/orchestration refusals (aligned to Grok’s capabilities), multilingual coverage, adversarial robustness, and rigorous consent handling.

Key takeaways:

• Build a decision‑tree codebook and hit κ ≥ 0.75 before scaling.
• Balance positives with hard negatives to estimate FPR credibly.
• Compute PPV/FPR with Wilson/Jeffreys 95% CIs per slice and publish macro/micro rollups.
• Lock model/version/policy builds and randomize runs for reproducibility.
• Redact operational details and protect annotators.

Next steps:

• Draft your codebook and run a 300‑item pilot this week.
• Stand up schemas, redaction, and quality gates.
• Recruit and train annotators; schedule adjudication and audits.
• Lock your test harness and compute per‑slice CIs; publish with a methods appendix.

Looking ahead, open leaderboards and shared protocols will enable apples‑to‑apples comparisons. Until then, a disciplined, 30‑day benchmark—built on clear definitions, careful annotation, and statistically sound intervals—can provide the trustworthy signal your stakeholders need. 🧪

Sources

• https://x.ai/blog/grok-1 — Grok‑1 Announcement (xAI). Relevance: Establishes Grok as an LLM focused on text reasoning rather than native media generation.
• https://x.ai/blog/grok-1.5 — Grok‑1.5 (xAI). Relevance: Documents model/versioning for reproducible testing and text‑centric capabilities.
• https://x.ai/blog/grok-1.5v — Grok‑1.5V (xAI). Relevance: Clarifies image understanding (perception) vs generation, guiding modality scoping for the benchmark.
• https://github.com/xai-org/grok-1 — grok‑1 (xAI GitHub). Relevance: Public materials lack deepfake‑prompt PPV/FPR with CIs, underscoring the need for an external benchmark.
• https://openai.com/policies/usage-policies — OpenAI Usage Policies. Relevance: Shows policy framing without public deepfake‑prompt PPV/FPR with confidence intervals.
• https://openai.com/index/dall-e-3 — DALL·E 3 (OpenAI). Relevance: Highlights generation guardrails but not per‑slice PPV/FPR with CIs for deepfake prompts.
• https://deepmind.google/technologies/synthid/ — SynthID (Google DeepMind). Relevance: Provenance/watermarking tech, not a moderation‑precision benchmark; motivates differentiation.
• https://ai.meta.com/research/publications/llama-guard-2/ — Llama Guard 2 (Meta). Relevance: Reports general safety metrics, not deepfake‑prompt PPV with CIs as specified here.
• https://www.anthropic.com/news/claude-3-family — Claude 3 Family Overview (Anthropic). Relevance: Discusses safety/red‑teaming without the requested deepfake‑prompt PPV/FPR with CIs.
• https://jailbreakbench.github.io/ — JailbreakBench. Relevance: Illustrates adversarial prompting approaches, informing dataset adversarial variants.
• https://github.com/thu-coai/MM-SafetyBench — MM‑SafetyBench (GitHub). Relevance: Multimodal safety benchmark context; inspires but does not provide the PPV/FPR CI reporting required here.

Sources & References