Cross-Border Deepfake Verification & Information Integrity Engine
Countering digital disinformation across regional ASEAN financial and public sectors requires automated detection of manipulated media and synthetic press releases. Build an information verification agent that evaluates claim authenticity using multimodal retrieval models. Produce reliable truth confidence scores with detailed evidence citation.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a multimodal claim verification engine for cross-border digital information integrity in ASEAN.
How work is evaluated
Evaluates truth stance classification accuracy and evidence document citation validity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
verdict_correctness
Ensures false claims are accurately refuted by the agent.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
stance_classification_f1
F1 score across stance classification categories. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement multimodal embedding tools to cross-check text claims against verified official database archives
Detect deepfake audio/video transcript manipulation markers
Calculate information veracity index scores based on multi-source stance classification
Export structured evidence packages formatted for newsroom and public sector fact-checkers
Reference links and supporting material
Synthetic dataset of news claims and corresponding authoritative government press releases.
How this agent runs
Evaluates truth stance classification accuracy and evidence document citation validity.
Challenge input
JSON claim statement and corpus reference
Python Standard Library
Base runtime environment for classification logic.
Evaluated output
JSON stance classification and confidence score
- Ensures false claims are accurately refuted by the agent.
- F1 score across stance classification categories. • target: 0.95 • range: 0-1
- Benchmark: InfoIntegrity-Fact-Bench
- Stance Classification F1 target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Modal · Optional
- Aim · Required
- Refly.AI · Optional
- Modal · Optional
Evaluation contract
- verdict_correctness · Weight 1
- stance_classification_f1 · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.