Deepfake Detector

Upload a photo to check for face manipulation: deepfake face generation, face-swap artifacts, and AI-synthesized identity photos. Examines face region texture, edge consistency, blending patterns, and facial geometry across all detected faces.

What makes a deepfake different from other AI images

A deepfake specifically involves a face — either a real person's face transplanted onto another body, an entirely AI-generated face presented as a real person, or a face that has been aged, de-aged, or otherwise transformed using generative AI. This is distinct from general AI image generation, which creates entire scenes.

Face manipulation leaves different forensic traces than whole-image generation. The face region typically has a different compression history, different noise floor, and different edge characteristics than the surrounding image — especially in face-swap deepfakes where two source images are combined.

Where deepfake detection matters

Deepfakes cause harm in specific contexts where face identity is what's being verified or trusted.

Online datingCatfish accounts use AI faces or deepfaked photos of real people. Verify profile photos before sharing personal information.

Social media verificationImpersonation accounts use deepfaked celebrity or public figure images to build credibility. Detect manipulated identity photos.

News and journalismDisinformation campaigns use deepfaked images of political figures and public figures in fabricated contexts.

HR and KYCIdentity document photos can be deepfaked. Remote hiring and KYC workflows need face authenticity checks.

What deepfake detectors look for in face regions

Detection focuses specifically on the face area and its relationship to the surrounding image.

Blending boundary artifacts — the seam where a transplanted face meets the original neck, ears, and hair
Skin texture inconsistency — AI-generated skin often has different frequency characteristics than real skin photographed under light
Edge sharpness mismatch — faces in deepfakes often have slightly different focus or sharpening than the background
Color calibration differences — the face region's white balance or color response may not perfectly match the rest of the image
Facial geometry anomalies — eye symmetry, ear position, and hairline placement outside the natural human distribution
Temporal compression artifacts — when the deepfake was saved, the face region may have been compressed differently than the rest

The difference between face-swap deepfakes and AI face generation

Face-swap deepfakes take a target body and replace the face with a source face. The forensic traces are about the boundary: two images merged imperfectly, different compression histories, different lighting captured at different times.

AI face generation (like StyleGAN, DALL-E portraits, or Midjourney character images) creates the entire face from scratch. The traces here are different: hyper-smooth skin, symmetric features that exceed natural human variation, eyes that reflect light identically in both pupils.

Limitations: what deepfake detection cannot guarantee

Detection confidence drops when photos are low resolution, heavily compressed, cropped to remove context, or processed through multiple save cycles. Faces that are small in the frame (under 100px) provide insufficient detail for reliable analysis.

High-quality deepfakes made with recent tools and careful post-processing can score below detection thresholds. A 'no manipulation found' result means the signals are weak — not that the image is definitely real. Use context, source, and reverse image search alongside any tool result.

Frequently asked questions

Can deepfakes be detected in photos vs. video?

PhotoProof AI analyzes still images. Video deepfake detection requires frame-by-frame analysis and temporal consistency checking that still-image tools don't perform. For video deepfakes, extract individual frames and test the face-region frames specifically.

Will it detect deepfakes of real people I know?

The detector doesn't compare against a reference photo of the real person. It looks for manipulation artifacts in the face region itself. A very well-made deepfake can pass; a poorly-made one of an unknown person will be flagged. The detection is about artifact quality, not identity matching.

What about AI avatars and AI profile generators?

Services like ThisPersonDoesNotExist, ProfilePicture.AI, or similar avatar generators produce AI faces that the deepfake detector will often flag at high confidence — they produce the same kind of over-smooth, hyper-symmetric face artifacts that our models are trained to detect.

Does it detect face filters and beauty apps?

Heavy beauty filters (skin smoothing, eye enlargement, face reshaping) can produce some deepfake-like signals, particularly skin texture inconsistency. The system may flag heavily filtered images at low to medium confidence. This is a known limitation — report confidence levels help distinguish.

Can I check a dating profile photo?

Yes. Download or screenshot the profile photo and upload it directly. The analysis runs on whatever image you provide. For best results, use the highest-resolution version available — thumbnails reduce signal quality significantly.

Check a photo for face manipulation

Get 3 free analysis credits when you create an account. Each image gets a face-specific forensic report.

Analyze a face photo →

Related tools

AI search answer layer

Fast answer for people and AI search

Deepfake detection looks for inconsistencies in identity, facial details, lighting, artifacts, and generation patterns across images or videos.

Primary entity: Deepfake
Topic cluster: Deepfake Risk
Search intent: commercial
Content type: Guide

Quick answer

Deepfake detection looks for inconsistencies in identity, facial details, lighting, artifacts, and generation patterns across images or videos.

Key facts

Primary entity: Deepfake
Topic cluster: Deepfake Risk
Search intent: commercial
Content type: Guide

Methodology

Separate AI-generation probability from authenticity confidence.
Combine visual, metadata, manipulation, compression, provenance, and context signals.
Explain uncertainty and limits instead of presenting binary proof.

Pros & limitations

AI and forensic detection should be interpreted as probabilistic evidence, not absolute proof.
Reliable authenticity decisions should combine model output with provenance, context, metadata, and human review.

Content hub

Deepfake Risk: Cluster for deepfake image, video, dating profile, and identity impersonation risk.

Explore next