Build a Real-Time Deepfake Voice & Video Verification Pipeline with LlamaIndex
High-value wire transfers are increasingly targeted by AI voice clone and video deepfake impersonation attacks. Build a multi-modal verification agent using LlamaIndex and spectrographic analysis tools to analyze caller audio streams and intercept deepfake authorization attempts with < 1% false acceptance.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize LlamaIndex audio-visual multimodal connectors and spectrographic verification tools to defend high-value wire transfers against deepfake attacks.
How work is evaluated
Evaluates False Acceptance Rate (FAR) and synthetic audio detection precision across real and AI-cloned speech samples.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
low_far_test
False Acceptance Rate must remain under 1.0%.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
detection_accuracy
Overall accuracy in classifying synthetic vs authentic speech. • target: 0.98 • range: 0.9-1
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 multi-modal data indexes in LlamaIndex for audio spectral feature parsing
Detect synthetic voice artifacts (phase discontinuity, unnatural prosody)
Build dynamic step-up authentication triggers for flagged high-value authorizations
Achieve sub-1% False Acceptance Rate (FAR) on zero-day synthetic audio samples
Reference links and supporting material
Dataset of 500 authentic voice samples and matching AI voice clones generated via state-of-the-art TTS models.
Challenge setup
Evaluates False Acceptance Rate (FAR) and synthetic audio detection precision across real and AI-cloned speech samples.
Challenge input
WAV audio file payload or spectral feature array of call authorization
LlamaIndex
Supports multimodal feature indexing and rapid query execution.
Evaluated output
JSON authentic/synthetic classification, confidence score, and liveness metric
- False Acceptance Rate must remain under 1.0%.
- Overall accuracy in classifying synthetic vs authentic speech. • target: 0.98 • range: 0.9-1
- Benchmark: ASVspoof Deepfake Benchmark
- Detection Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Llama Index · Required
- LlamaIndex · Optional
- Llama 3.2 · Optional
Evaluation contract
- low_far_test · Weight 1
- detection_accuracy · Weight 1
Metrics
- detection_accuracy · Target 0.98
Benchmarks
- ASVspoof Deepfake Benchmark
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: WAV audio file payload or spectral feature array of call authorization
Output: JSON authentic/synthetic classification, confidence score, and liveness metric
Execution harness: Python
Evaluation tools
- LlamaIndex · Multimodal Indexing Framework
Recipe freeze marker
No recipe freeze time is recorded.