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Challenge

Build an AutoGen Multi-Agent Image Forensics Inspector with DeepSeek R1

Widespread image duplication and validation anomalies undermine published biomedical scientific papers. Develop an AutoGen multi-agent system powered by DeepSeek R1 reasoning models to audit Western blot figures, cross-check metadata, and output reproducible forensic audit reports.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Deploy an AutoGen multi-agent system using DeepSeek R1 reasoning capabilities to detect image manipulation and fraud in scientific literature.

Delivery guide

How work is evaluated

Evaluation

Tests AutoGen multi-agent system detection of spliced or altered blot image samples.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

manipulation_detection_accuracy

Ensures true positive manipulation detection score exceeds cutoff threshold.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

forensic_f1_score

F1 score across modified and pristine scientific image test sets. • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Configure Microsoft AutoGen multi-agent workflows with specialized persona roles

  • Utilize DeepSeek R1 reasoning capabilities to analyze image duplication and feature splicing

  • Build visual contrast analysis tools to identify altered antibody validation figures

  • Generate standardized forensic inspection reports detailing manipulation likelihood scores

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 50 scientific figure panels consisting of 25 unaltered original images and 25 synthetic/spliced images with manipulation ground truth.

How this agent runs

Tests AutoGen multi-agent system detection of spliced or altered blot image samples.

Challenge input

JSON containing base64 image data or file path and figure caption text.

AutoGen

Orchestrates conversational sub-agents.

DeepSeek R1

Provides deep step-by-step reasoning for image forensics.

Evaluated output

JSON containing manipulation_detected (boolean), confidence_score (float), and flagged_regions (list).

Checks for
  • Ensures true positive manipulation detection score exceeds cutoff threshold.
  • F1 score across modified and pristine scientific image test sets. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: Biomedical Image Integrity Benchmark (BIIB)
  • Forensic F1 Score target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • DeepSeek · Optional
  • AutoGen · Optional
Policy Serving
  • DeepSeek R1 · Required

Evaluation contract

  • manipulation_detection_accuracy · Weight 1
  • forensic_f1_score · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build an AutoGen Multi-Agent Image Forensics Inspector with DeepSeek R1