Disclaimer

This Disclaimer outlines the limitations, appropriate use, and liabilities relating to gfpgaan.com. This site publishes independent documentation about GFPGAN — an open-source AI face restoration model developed by Tencent ARC, with whom we have no affiliation — and hosts a free browser-based photo enhancer. By using this site you acknowledge and accept the conditions described below.

1. AI-Generated Output — No Accuracy Guarantee

GFPGAN is an artificial intelligence model that reconstructs facial detail in degraded images. While it performs well on the benchmarks reported in its original paper, AI-generated outputs are inherently probabilistic and may not perfectly replicate the original subject in all cases.

Restored images produced by GFPGAN are AI-reconstructed approximations, not verified photographic records. Specific features such as eye color, fine skin texture, hair color gradients, or subtle expression details may differ from the original, especially when:

• The source image is extremely low resolution (below 40×40 pixels for the face region)
• The face is heavily occluded by hands, hats, hair, or other objects
• The subject is photographed at extreme angles (profile, upward/downward tilt beyond 45°)
• The source image contains severe distortion, artistic filters, or non-photographic degradation

We make no warranty that GFPGAN output accurately represents any specific individual. Do not use GFPGAN output as evidence, official documentation, or identity verification in any context.

2. Not a Professional Service

GFPGAN is a free, general-purpose AI tool. It is not a professional forensic, archival, medical, or legal image processing service. Outputs are suitable for personal use, creative projects, and general photo restoration — they are not intended to serve as:

• Forensic evidence or exhibit material in legal proceedings
• Identity verification or authentication for official purposes
• Medical imaging or diagnostic enhancement
• Biometric data for security systems
• Official archival records for historical or governmental use

If you require professional-grade, accuracy-verified image restoration, consult a qualified specialist.

3. Responsible Use of Restored Images

You are solely responsible for how you use the images you restore. Before sharing, publishing, or distributing any restored image, you must:

• Obtain consent from the person depicted, where required by applicable law or platform policy
• Disclose AI enhancement clearly where context requires transparency (journalism, social media, official records)
• Avoid misrepresentation — do not present AI-restored images as unaltered original photographs
• Respect third-party rights — do not restore and republish copyrighted images without authorization

Creating or distributing non-consensual intimate images, deepfakes designed to deceive, or restored images intended to harass or defame any person is strictly prohibited under our Terms of Service and may violate applicable law.

4. Identity and Likeness

GFPGAN uses a generative facial prior (StyleGAN2) to reconstruct detail. In cases of severe degradation, the model makes its best reconstruction based on learned face statistics — not the specific individual's actual appearance. This means:

• The output is a plausible face reconstruction, not a forensically verified likeness
• On severely degraded inputs, the output should be treated as an AI-assisted approximation
• Identity verification based on GFPGAN output alone is not reliable or recommended

5. Third-Party Model and Content Disclaimer

The underlying GFPGAN model is open-source software developed by Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan at Tencent ARC; its licence terms are stated in the official repository. We did not develop the model, we do not host or run it, and we are not affiliated with its authors. This site documents the model and hosts a separate browser-based image filter (see section 10). We make no claims about the model’s performance beyond what its authors have published, and any benchmark figures we cite are attributed to their source.

6. Blog and Informational Content

Blog articles, comparisons, and guides published on this site are for informational purposes only. Benchmark numbers, model comparisons, and performance claims are based on publicly available research and our testing at the time of writing. AI models evolve rapidly — figures may become outdated. Do not rely solely on our blog content for technical decisions in production environments. We recommend consulting the original research papers for authoritative performance data.

7. No Liability for Outputs

We are not liable for any outcomes arising from your use of GFPGAN-restored images, including but not limited to: reputational damage, misidentification, legal consequences, or emotional distress. The tool is provided as-is for personal and creative use. All use is at your own risk and responsibility.

8. External Links

This site links to external resources including GitHub repositories, academic papers, and third-party tools. These links are provided for reference only. We do not control external sites and are not responsible for their accuracy, availability, or content. Linking to an external resource does not constitute endorsement.

9. Advertising Disclosure

This site is free to read and is funded by advertising. We display ads through Google AdSense and no other network. Advertisers have no influence over what we publish: they do not review content before publication, cannot request changes, and cannot buy placement in a comparison or a recommendation. We do not accept payment for favourable coverage, sponsored posts, or link insertions.

We currently use no affiliate links. If that changes, each affected page will carry a disclosure and it will be listed in our editorial policy. Ads shown here are selected by Google, not by us — we do not endorse advertised products, and an ad appearing next to an article is not a recommendation. For the data implications of advertising cookies, see our Privacy Policy and Cookie Policy.

10. Scope of the Free Tool on This Site

The photo enhancer hosted on this site is a classical image-processing filter — tone mapping, colour recovery, artifact smoothing, and an unsharp mask — implemented in JavaScript and executed entirely in your browser. It is not the GFPGAN neural network, performs no neural inference, and cannot reconstruct facial detail that is not already present in your image. Sections 1 and 4 above describe GFPGAN the model, which you can run yourself by following our installation guide.

11. Contact

If you have questions about this Disclaimer or encounter an issue with our service, please contact us at hello@gfpgaan.com or through our Contact page.