DALL-E 3, Midjourney v6, Stable Diffusion XL, and Adobe Firefly now produce images that are indistinguishable from photos at first glance. This has practical consequences: fabricated product images, fake event photos, synthetic profile pictures, and manipulated evidence in professional contexts. The question of whether an image is synthetic is no longer theoretical.

The challenge: image detection is significantly harder than text detection. Humans leave statistical fingerprints in writing that persist through editing. AI-generated images, especially after compression and resizing, lose many of their artifacts. Detection accuracy for images is lower than for text, and anyone building a workflow that depends on it needs to understand the limits.

What AI image detectors actually look for

Forensic image analysis combines multiple signals. No single artifact is conclusive. A reliable detector uses several methods simultaneously and produces a confidence score, not a binary verdict.

Texture inconsistency

Diffusion models blend pixels probabilistically. Close inspection often reveals micro-texture that looks uniform in a way that is statistically improbable in camera-captured images.

Anatomical errors

Hands, teeth, ears, and reflections in eyes are notoriously difficult for models. Current generation models have improved dramatically but still produce errors under scrutiny.

Metadata absence

Authentic photos contain EXIF metadata (camera model, GPS, aperture, shutter speed). AI-generated images ship with no EXIF, or fabricated metadata inserted by post-processing.

Frequency artifacts

GAN and diffusion-based images leave characteristic patterns in the Fourier frequency domain. These are invisible to the naked eye but detectable via spectral analysis.

Noise profile mismatch

Camera sensors introduce specific noise patterns at high ISO. AI images synthesized at apparent high-ISO conditions lack the correct noise profile, creating a detectable mismatch.

Semantic inconsistency

Text in images, clock faces, and complex signage often contain gibberish in AI-generated images. The model renders plausible-looking letterforms without semantic grounding.

How different models compare in detectability

Not all AI image generators leave the same artifacts. Detection tools perform differently depending on which model produced the image:

DALL-E 3

Relatively detectable. OpenAI adds C2PA content credentials (cryptographic provenance metadata) to DALL-E 3 outputs through the API and ChatGPT. When credentials are intact, detection is near-certain. After social media recompression or screenshot, credentials are stripped and detection falls back to artifact analysis, where DALL-E 3 still leaves characteristic softness in fine details.

Midjourney v6

Harder to detect than DALL-E. Midjourney v6 images have a distinctive aesthetic quality that trained human reviewers recognize, but automated detectors struggle more with v6 than v5 outputs. The improved photorealism reduces the frequency-domain artifacts that earlier versions left behind.

Stable Diffusion XL / FLUX

Most variable. Open-source models can be fine-tuned to target specific aesthetics, and community fine-tunes have partially eliminated the base model artifacts. Detection performance degrades significantly on heavily fine-tuned variants. Detection tools trained on base SDXL outputs may miss fine-tuned variants entirely.

Adobe Firefly

All Firefly outputs include Content Authenticity Initiative (CAI) metadata. When metadata is intact, Firefly images are reliably identifiable via credential verification. Firefly is also trained exclusively on licensed content, which makes it slightly less common in contexts where provenance questions arise.

Accuracy benchmarks: what to expect

Honest benchmarking of image detection tools is less mature than for text. Published numbers vary widely depending on the test set, compression level, and which models were included. Rough estimates based on published evaluations and Airno internal testing:

Condition	Accuracy	Notes
Original file, high-quality model	78–88%	Best case
Post-social-media compression	55–70%	JPEG artifacts interfere
Screenshot of AI image	45–65%	Metadata gone, artifacts reduced
Photo of AI print / screen	~40%	Near-random; don't rely on detection
AI image + real photo composite	30–55%	Depends on manipulation extent

Ranges reflect variation across model types. Text detection accuracy is significantly higher (90-98%) for the same input conditions. Image detection is an inherently harder problem.

The C2PA standard: cryptographic provenance

The long-term answer to AI image provenance is cryptographic signing, not artifact detection. The Coalition for Content Provenance and Authenticity (C2PA) has developed a standard where image generators embed a cryptographically signed manifest that records when, where, and how an image was produced. This manifest travels with the file.

Participants include Adobe, Microsoft (DALL-E via Azure), Leica (camera-side signing for authentic photos), and several news organizations. When a C2PA manifest is present and verifiable, you know both who created the image and which tool produced it. When it is absent, you fall back to artifact analysis.

The limitation: C2PA metadata is stripped by most social media platforms, messaging apps, and basic image editors. An AI-generated image that has been through Instagram has no C2PA manifest left. Artifact-based detection remains necessary for the majority of images circulating online.

Manual detection: what to look for yourself

Even without a tool, trained eyes can spot common AI image tells. These are increasingly reliable for current-generation models:

→Zoom into hands: look for extra fingers, fused digits, or anatomically impossible joints
→Look at text in the image: road signs, clothing logos, books. AI text is usually nonsense letterforms
→Check the background: AI models often render consistent-looking but nonsensical details (books with unreadable spines, posters with blurred content)
→Look for bilateral symmetry errors: faces that are almost but not quite symmetrical in an uncanny way
→Check lighting consistency: shadows and reflections that don't match the apparent light source
→Look at hair near edges: diffusion model hair often has an unnaturally smooth edge blending into the background
→Right-click and check file properties: zero EXIF data on a supposed photograph is a strong signal

How Airno handles image detection

Airno runs uploaded images through a multi-method forensic pipeline that checks frequency-domain artifacts, metadata consistency, noise profile analysis, and a CNN-based classifier trained on synthetic and authentic image pairs. Results are returned as a confidence score (0-100) with method-level breakdown, the same pattern used for text detection.

Honest caveat: image detection accuracy is lower than text detection. Airno shows 78-85% accuracy on clean original files. Treat borderline scores (35-65%) as inconclusive and verify through other means such as reverse image search or provenance metadata if available.

Try Airno image detection free