AI Image Detector: Check If an Image Is AI-Generated
Upload an image to check for AI generation, manipulation and authenticity signals. AIDetector.cx analyzes visual patterns, metadata and available provenance without treating one signal as absolute proof.
What Your Image Analysis Result Means
AIDetector.cx evaluates probabilistic signals rather than making rigid binary guesses. Every score is calibrated to prevent false accusations while surfacing subtle generative artifacts.
High convergence across high-frequency Fourier residuals, synthetic pixel noise distributions, and repetitive generative textures (e.g. skin over-smoothing, unnatural pupil geometry).
Confidence Score >75%: Strong statistical evidence of diffusion generation.
Pixel noise patterns correlate with authentic camera sensor silicon (Bayer filter array, Poisson-Gaussian noise distributions) without generative frequency artifacts.
Confidence Score <25%: Low synthetic probability with natural sensor coherence.
The background shows natural optical noise, but specific regions (such as a face, hand, or inserted object) exhibit disparate Error Level Analysis (ELA) divergence and edge boundary warping.
Indicates generative inpainting or composite manipulation.
Calibrated Uncertainty & Limitations
How AIDetector.cx Analyzes an Image
Our multi-signal forensic pipeline fuses pixel-domain, frequency-domain, and cryptographic metadata checks to deliver transparent, explainable results.
1. Visual Artifacts & Textures
Evaluates anatomical coherence (corneal glints, ear symmetry, fingernails), background depth continuity, and fine prompt-rendering artifacts.
2. Error Level Analysis (ELA)
Computes compression differential maps across 8x8 DCT blocks to identify spliced regions saved at differing quantization quality levels.
3. Laplacian Residual Noise
Isolates high-frequency sensor noise from structural image content using spatial Laplacian filters to detect diffusion model grid regularities.
4. C2PA & Provenance Validation
Parses binary JUMBF boxes to verify Adobe Content Credentials, camera hardware signatures, and digital tamper status against root CAs.
AI-Generated vs. Edited vs. Authentic Images
Conventional photo adjustments (like Photoshop retouching or color grading) are not generative deepfakes. Here is how our detector distinguishes each category:
| Image Category | Primary Forensic Characteristics | Detector Verdict | ELA Behavior |
|---|---|---|---|
| Fully AI-Generated | Diffusion residual noise, absent EXIF sensor pipeline, synthetic frequency peaks | Likely AI-Generated | Uniform synthetic compression error map |
| Generative Inpainting / Face Swap | Boundary blending artifacts, localized frequency divergence on facial region | Suspected AI Manipulation | Localized high-divergence error spike |
| Photoshop / Global Retouching | Uniform color grading, unwarped edges, coherent camera noise pattern | Conventional Editing | Low localized divergence without synthetic noise |
| Authentic Camera Capture | Consistent sensor silicon noise, valid EXIF header, natural lighting vectors | Likely Authentic | Evenly distributed natural error pattern |
| Heavy Social Re-compression | Extreme quantization noise, downsampled pixel grid (<400px), stripped EXIF | Inconclusive | Severe quantization masking fine residuals |
Why AIDetector.cx Protects Against False Positives
Many free tools falsely accuse real photographers because compressed JPEGs look noisy. AIDetector.cx Balanced Mode incorporates compression compensation algorithms and lists real benign conditions:
Compression Compensation
Compensates for WhatsApp, WeChat, and TikTok aggressive JPEG re-compression so quantization artifacts are not mislabeled as AI noise.
Multi-Signal Convergence
Never makes an AI verdict based on a single heuristic. Requires multi-layer consensus between ELA, frequency residuals, and metadata.
Legitimate Filter Recognition
Recognizes digital illustrations, smartphone Night Mode computational photography, and cosmetic smoothing as non-generative.
How to Use the Free AI Image Detector Online
Verify any photo in three fast, evidence-based steps.
Upload or Paste Image
Drag and drop your file, paste directly from clipboard, or enter an image URL. Supports JPEG, PNG, WEBP, AVIF, HEIC up to 25MB.
Choose Detection Mode
Select Balanced Mode for high false-positive resistance or High-Sensitivity to detect minor inpainting traces.
Review Verdict & Export
Inspect the calibrated confidence indicator, ELA heatmap, and C2PA signature. Download a PDF certificate or JSON payload.
What Image Generators Can Be Analyzed?
The detector evaluates images from known and unknown generation workflows. Performance can vary by model version, post-processing, and compression.
AI Image Detection for KYC and Fraud Screening
Designed to support KYC and fraud-screening workflows. Automated image detection provides early-warning risk signals to protect account onboarding, payment security, and insurance claims against synthetic identities.
Synthetic ID & Selfie Detection
Flags generated face avatars (StyleGAN, FLUX portraits) and digitally modified government ID photos during customer onboarding.
Insurance & Receipt Verification
Identifies generative inpainting on vehicle damage claims, property loss photographs, and altered medical receipts.
Audit Trails & Human Escalation
Generates tamper-evident SHA-256 evidence certificates for compliance review. Risk signals guide human compliance officers rather than enforcing unreviewed automated bans.
AI Image Detection API
Integrate enterprise-grade image authenticity checks directly into your content moderation queue, marketplace upload pipeline, or KYC workflow.
The AIDetector.cx REST API allows you to submit binary images or public URLs and receive structured JSON results including AI probability, Error Level Analysis metrics, C2PA claims, and detected model signatures.
- Single & batch image submission (
POST /v1/images/detect) - Zero retention in-memory processing guarantees data confidentiality
- Standardized API keys, credit billing, and webhook notifications
{
"status": "success",
"sha256": "8f3b20...91a2",
"ai_generation": {
"verdict": "Likely AI-generated",
"score": 88,
"confidence_level": "High",
"detected_signatures": ["Midjourney v6", "Diffusion Residuals"]
},
"manipulation": {
"detected": false,
"ela_max_divergence_pct": 2.4
},
"c2pa": {
"has_manifest": false
}
}How Accurate Are AI Image Detectors?
Detection accuracy is not a single static number. Performance varies dynamically across image resolution, generative model architectures, compression, and post-processing filters.
Detector output must always be interpreted as probabilistic forensic evidence, never as absolute judicial proof of human identity. When an image contains clean optical noise from a known camera sensor, confidence exceeds 95%. When an image has been saved multiple times across social media networks, fine diffusion residuals degrade.
Key Factors Increasing Accuracy:
- Original camera captures with unstripped EXIF
- Uncompressed PNG or high-bitrate WebP/JPEG
- High native resolution (>1024x1024px)
- Presence of signed C2PA Content Credentials
Key Factors Degrading Signals:
- Aggressive social re-compression (e.g. WhatsApp)
- Extreme downsampling (<400px width)
- Heavy Gaussian blurring or artistic filtering
- Screen captures / phone photos of screens
Can AI Image Detectors Be Bypassed?
Adversarial techniques and severe compression can degrade individual detection signals. Here is why multi-signal fusion provides resilient protection without relying on a single failure point:
Evasion Techniques
Adversaries often apply subtle film grain, heavy compression, color grading, or cropping to disrupt frequency-domain Fourier peaks and confuse single-heuristic detectors.
Multi-Layer Fusion
AIDetector.cx combines Error Level Analysis (ELA), Laplacian high-frequency residuals, EXIF integrity, C2PA claims, and lighting vector consistency so no single filter blinds the system.
Safe Uncertainty Handling
When an image is too heavily distorted to evaluate with scientific confidence, our engine returns an honest Inconclusive verdict rather than generating an unreliable guess.
What Makes a Reliable AI Image Detector?
When evaluating image authenticity tools, enterprise and consumer users should look for objective criteria rather than marketing claims:
Conservative default thresholds (Balanced Mode) ensure real photographers and authentic historical archives are not falsely accused.
Interactive Error Level Analysis (ELA) and Laplacian noise heatmaps allow users to see exactly which pixel clusters triggered flags.
Direct parsing of Coalition for Content Provenance and Authenticity (C2PA) JUMBF binary boxes checks digital signatures against trusted root CAs.
Identifies localized modifications where a real background was merged with a generated subject or swapped face.
Guarantees that uploaded files are processed in ephemeral memory without permanent storage or training use.
Provides scalable JSON endpoints for automated platform moderation and customer onboarding pipelines.
Who Uses AIDetector.cx AI Image Detector?
From breaking newsrooms to financial compliance teams, our forensic image suite serves diverse verification workflows:
Journalists & Fact-Checkers
Verify viral social media images, citizen journalism footage, and press releases before publishing breaking stories.
Artists & Photographers
Protect original visual portfolios against unauthorized AI scraping and demonstrate authentic human authorship.
Marketplaces & E-Commerce
Screen fraudulent seller listings, synthetic mockups, and non-existent inventory on peer-to-peer commerce sites.
KYC & Identity Fraud Teams
Identify AI-generated onboarding selfie avatars, manipulated driver license portraits, and synthetic bank documents.
Insurance Claim Adjusters
Inspect submitted vehicle collision photos, property damage estimates, and repair receipts for generative inpainting.
Platform Moderation Teams
Automate platform trust and safety screening using high-throughput REST API webhooks to enforce synthetic media policies.
Privacy and Uploaded Images
We uphold strict data confidentiality and zero-retention principles across all image analysis workflows:
Frequently Asked Questions About AI Image Detection
Detailed, technically accurate answers to common questions about artificial intelligence image detection, Error Level Analysis, and verification.