How to Read Your Scan Results
Each media type is evaluated using a different set of specialized detectors. Your final score is an aggregate of these models, weighted by reliability and designed to give you a clear, actionable assessment.dels Reality Defender uses
Every analysis includes:
A final probability score (0–100%) showing the likelihood the content is manipulated or AI-generated
A severity label (Low, Medium, High, Critical)
Breakdown of contributing models, including each model’s score and what it detects
A combined explanation showing how the models produced the final conclusion
Each detection model specializes in a different type of manipulation or synthesis method. Some models look at artifacts, others at context, content patterns, temporal dynamics, or linguistic signals.
Interpreting the Confidence Scale
RealScan simplifies all model outputs into a single, human-readable Confidence Scale. It applies to both the amount of generative content and the likelihood of manipulation.
Level | Meaning | What To Do |
Minimal | No meaningful signs of AI generation or manipulation. Authenticity appears high. | Safe to consider authentic; continue standard verification. |
Low | Weak or isolated AI indicators found, but not enough to conclude manipulation. | Review manually; check for compression or lighting issues that might cause false signals. |
Moderate | Multiple AI signatures detected, or models disagree. | Proceed with caution; verify source and metadata. |
High | Strong, repeated evidence of AI generation or manipulation. | Treat as likely synthetic. Recommend escalation or secondary validation. |
Tip: A higher confidence level means more evidence of AI involvement, not necessarily that the entire file is fake. Authentic media that’s been lightly enhanced (e.g., color corrected or denoised) may still trigger a Low confidence result.
🔈 Understanding Audio Results
When analyzing audio, Reality Defender evaluates indicators of voice synthesis, voice cloning, or audio splicing.
Models used
Advanced
Detects AI-generated audio using a large foundation model trained on highly diverse synthesized-speech datasets.
Looks for:
Neural vocoder artifacts
Frequency-domain anomalies
Model-specific generation fingerprints
Generalizable
Detects AI-synthesized audio using broad linguistic and stylistic cues found in real human speech.
Looks for:
Unnatural prosody
Overly consistent tone or pacing
Style mismatches common to audio LLMs
How the final score is determined
Both models produce an independent probability. The final result is a weighted combination of the two, with Advanced contributing more heavily when audio quality is high.
📸 Understanding Image Results
Image detection evaluates synthetic or manipulated visual signals across GAN, diffusion, and traditional image-editing workflows.
Models used
Context Aware
Detects deepfake manipulation by evaluating the full visual context of the image.
Looks for:
Lighting inconsistencies
Abnormal physical context
Semantic mismatches
Visual Noise Analysis
Detects fake images by analyzing the texture and distribution of visual noise.
Looks for:
Diffusion-grid artifacts
Upsampler inconsistencies
GAN-style frequency patterns
GANs
Detects faces generated or manipulated using GAN-based methods.
Looks for:
StyleGAN fingerprints
Resolution-frequency mismatches
Geometric distortions
Diffusion
Detects images created by diffusion models (ex: Midjourney, SDXL).
Looks for:
Diffusion sampling artifacts
Uniform noise fields
Overly smooth or algorithmic textures
Faceswaps
Detects traditional and modern faceswap-based manipulations.
Looks for:
Boundary inconsistencies
Compositing artifacts
Identity mismatches
How the final score is determined
Each model contributes a score, weighted depending on relevance (e.g., Diffusion and Visual Noise Analysis weigh more heavily for generative images; Faceswaps weigh more for portrait manipulation).
🎥 Understanding Video Results
Video analysis evaluates frame-level, temporal, and contextual indicators of deepfake generation.
Models used
Context Aware
Detects deepfake manipulation by evaluating the full visual and scene-level context.
Looks for:
Inconsistent lighting
Contextual anomalies
Scene-wide tampering indicators
Dynamics
Detects deepfake faces generated with various methods by analyzing temporal information.
Looks for:
Frame-to-frame inconsistencies
Motion artifacts
Lip-sync irregularities
Guided
Focuses on specific facial features known to differ between real and generated faces.
Looks for:
Eye-region anomalies
Facial microexpression inconsistencies
Local generation fingerprints
Universal
Detects deepfake faces generated across many methods (GANs, diffusion, hybrid approaches).
Looks for:
Global artifact patterns
Multimethod synthesis signals
How the final score is determined
Temporal models (Dynamics + Context Aware) take heavier weight for video, with Guided and Universal refining the final probability.
🔡 Understanding Text Results
Text detection evaluates whether text has been generated or heavily edited by a large language model (LLM).
Models used
Text Detector – Generative
Detects linguistic patterns characteristic of LLM-generated text.
Looks for:
Over-optimized phrasing
Statistical smoothness
Predictable structural patterns
Low-variance word choice
How the final score is determined
Since text has a single model type, the final score reflects the model’s probability directly.
Why Model Scores May Differ
Some models specialize in:
Certain generation methods (e.g., GANs, vocoders, diffusion)
Certain content types (faces, scenes, natural speech)
High- vs low-quality inputs
Different artifact families (noise, compression, temporal distortions, linguistic patterns)
It is normal for one model to score low while another scores extremely high—this is expected behavior and is incorporated into the weighted final score.
If Your Result Seems Unexpected
Content may test as manipulated due to:
Heavy compression or filtering
AI-assisted editing
Image upscaling or enhancement
Non-human voices (e.g., IVR, TTS, synthetic accents)
AI-generated copy mixed with human text
Blend of real and generated segments
If you have questions about a specific file, use the "Report an Issue" button at the top right corner of your Scan Results page, or contact support@realitydefender.com.
Downloading Reports
Every completed scan can be exported as a PDF or CSV report containing:
Upload timestamp
Detected modality and file metadata
Per-model results and triggered indicators
Final confidence rating and explanation
Reports are timestamped and can be shared internally for record-keeping or audits.