The Trust Center

Our Commitment to Truth

A transparent look at our methodology, privacy standards, and ethical AI principles. We believe that in a post-truth world, forensics must be as immutable as the facts they verify.

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How It Works

Neural Pattern Recognition

Our core engine utilizes a proprietary dual-pathway neural network. The first pathway scans for GAN (Generative Adversarial Network) signatures — the mathematical artifacts left behind by AI upscaling and blending algorithms. The second pathway focuses on biological inconsistencies: micro-expressions, unnatural light refraction in the cornea, and sub-pixel frequency anomalies.

FREQUENCY ANALYSIS

Detection of high-frequency noise patterns that differ from standard CMOS sensor noise.

TEMPORAL COHERENCE

Tracking frame-to-frame stability in video to identify temporal flickering common in deepfakes.

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Spectral Analysis

We analyze the light spectrum of every pixel. Synthetic media often fails to replicate the exact physics of light bouncing off organic surfaces, such as human skin or hair.

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Privacy First

Data Sovereignty Protocol

01

Local Edge Processing

Whenever possible, forensic analysis is performed locally in your browser environment. Your original high-resolution source files are never uploaded to our central servers for basic verification tasks.

02

No Storage Policy

In cases where cloud-assisted neural processing is required, files are stored in volatile memory (RAM) and purged immediately after the verdict is generated. We do not maintain a database of user-submitted media.

03

Zero-Knowledge Reports

The metadata extracted during analysis is encrypted using your session key. We only see the final probability score, ensuring complete forensic privacy.

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Ethics Statement

Our mission is to restore trust in visual media. We adhere to a strict ethical framework that prohibits the use of our technology for the creation of deepfakes or the profiling of individuals.

  • check_circleCommitment to Open Forensics methodology.
  • check_circleNeutrality in verdict generation, regardless of subject.
  • check_circleContinuous bias auditing for neural model training.
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