Image Forgery Detection Technical Breakdown

Our Image Forgery Detection suite goes deep into the pixel data of every upload. We use Error Level Analysis (ELA) to find areas with different compression levels, indicating a "copy-paste" or "slice" manipulation. Our engine also analyzes sensor noise patterns (PRNU) to verify if an image was really taken by a specific camera or generated by an AI model.

The Science & Methodology

Our verification processes are built on standard forensic methodologies. By analyzing data at the architectural level, we ensure that every result is backed by mathematical certainty rather than simple AI-generated guesses.

Utilizes pixel-level noise variance analysis and quantized JPEG ghosting to detect discontinuities in the underlying sensor fingerprint (PRNU). Forensic Methodology Overview

Operational Use Cases

Profile Verification

Verify if a dating profile picture has been digitally altered or AI-generated.

Media Validation

Validate news or social media imagery to ensure authentic, unedited reporting.

Fraud Detection

Spot modified documents or "sliced" identity cards in KYC flows.

Key Forensic Capabilities

• Error Level Analysis (ELA): Advanced algorithmic validation for ensuring digital integrity.
• JPEG Ghost Detection: Advanced algorithmic validation for ensuring digital integrity.
• Noise Variance Mapping: Advanced algorithmic validation for ensuring digital integrity.
• AI Artifact Recognition: Advanced algorithmic validation for ensuring digital integrity.
• Pixel Continuity Tracking: Advanced algorithmic validation for ensuring digital integrity.