AI Image Detection

ReviewerZero analyzes manuscript figures to detect AI-generated content and verify image provenance. Our system uses multiple methods at both the figure and panel level to identify synthetic imagery.

Why AI Image Detection?

AI image generation tools have become increasingly sophisticated, making it possible to create realistic scientific figures. This raises concerns about:

• Fabricated data - Synthetic images presented as real experimental results
• Misleading visualizations - AI-generated graphics that don't represent actual data
• Provenance questions - Uncertainty about whether images are authentic

Our detection system helps identify these issues before publication.

C2PA Verification

Content Credentials (C2PA) is a cryptographic standard for embedding provenance information directly into images.

What C2PA Provides

When images contain C2PA metadata, we can verify:

• Origin information - Where and how the image was created
• Editing history - What modifications were made and by whom
• AI-generation flags - Whether the image was created by AI tools
• Tool identification - Which software or AI model was used

Supported AI Tools

C2PA metadata can identify images from:

• DALL-E and OpenAI image generators
• Midjourney
• Stable Diffusion (when properly configured)
• Adobe Firefly
• Other C2PA-compliant tools

Verification Results

When C2PA metadata is present, it provides high-confidence information about image origin.

Machine Learning Detection

For images without C2PA metadata, our machine learning models analyze visual patterns to detect AI-generated content.

How It Works

1. Panel extraction - Individual panels are identified within figures
2. Feature analysis - Each panel is analyzed for AI-generation indicators
3. Scoring - Probability scores are assigned based on detected patterns
4. Classification - Panels are classified based on confidence thresholds

What We Detect

Our models identify patterns characteristic of AI-generated imagery:

• Texture anomalies - Unnatural smoothness or repetition
• Structural inconsistencies - Impossible geometries or perspectives
• Artifact patterns - Compression and generation artifacts specific to AI tools
• Statistical signatures - Pixel distributions typical of generative models

Panel-Level Analysis

Each panel within a figure is analyzed individually:

• Independent scoring - Every panel receives its own probability score
• Precise identification - Know exactly which parts of a figure are flagged
• Confidence indicators - Understand how certain each detection is

Understanding Results

Detection Categories

Confidence Scores

Results include probability scores:

False Positives

Some legitimate images may trigger detection:

• Computer-generated graphics - Intentional diagrams and illustrations
• Heavily processed images - Images with significant post-processing
• Scientific visualizations - Rendered 3D models or simulations

These can be dismissed if they represent legitimate content.

Managing Findings

Reviewing Detections

For each flagged image:

• View the confidence score and classification
• See which specific panels are flagged
• Compare with C2PA metadata if available
• Access the original image for manual inspection

Dismissing False Positives

If a detection is incorrect:

1. Review the flagged image in context
2. Click to dismiss the finding
3. Optionally add a note explaining why
4. The finding is removed from active results

Restoring Dismissed Findings

Dismissed findings can be restored at any time:

• Access the dismissed items list
• Select items to restore
• Items return to active results

Best Practices

For Authors

1. Use C2PA-compliant tools when possible
2. Clearly label any AI-generated or computer-rendered content
3. Disclose the use of AI tools in your methods section
4. Keep original, unprocessed versions of all images

For Reviewers

1. Check AI detection results for all figures
2. Pay special attention to high-confidence detections
3. Consider context - some AI use may be legitimate
4. Request clarification from authors when needed

For Publishers

1. Include AI detection in your screening workflow
2. Establish policies for AI-generated imagery
3. Require disclosure of AI tool usage
4. Document findings in your records

Integration with Other Features

AI Image Detection works alongside other ReviewerZero capabilities:

• Image Duplication Analysis - Also checks for copied or manipulated regions
• Figure Accessibility - Analyzes contrast and color-blind safety
• Statistical Checks - AI text detection for manuscript content

Related Resources

• Image Duplication Analysis - Detect manipulated figures
• Statistical Checks - AI text detection
• Platform Features - Platform capabilities