Banking & finance
Detect AI-generated or doctored images submitted during onboarding and review.
How the sector deploys itDeepImageGuard
Verify whether an image is edited or AI-generated. Upload an image directly, and it returns a clear real-or-fake verdict, supported by easy-to-understand visual evidence and plain-language reasoning anyone can follow
DeepImageGuard · workspaceImage received
sample-image.jpgno markings
- Received
- Preparing
- Verifying
- Evidence
- Result
Independent checks
- Visual traitswaiting
- Camera traceswaiting
- Synthetic signswaiting
- Provenancewaiting
- Scene checkswaiting
- Known mediawaiting
Running
Interface simulation — an illustration of the workflow, not a live analysis.
Product Walkthrough
0:000:00
What it is
A real-or-fake verdict, and the evidence behind it
Interface simulation — an illustration of the workflow, not a live analysis.
DeepImageGuard is an AI-powered image authenticity and digital forensics solution that detects AI-generated, manipulated, and authentic images with explainable evidence. It analyzes multiple independent forensic signals-including visual and frequency patterns, noise fingerprints, synthetic-media indicators, provenance, semantic analysis, and known-media matching to identify manipulation beyond what the human eye can detect. Instead of delivering only a REAL or FAKE prediction, it provides calibrated confidence, corroborated evidence, and forensic reasoning.
The verdict is never the whole answer. Designed for enterprise-grade detection of AI-generated deepfake images, it can be used through the web application or integrated with existing systems. It supports organizations with fraud prevention, media verification, trust and safety, compliance, and digital investigations where understanding the authenticity of an image matters.
A fake has to fool several independent forensic signals together, so none slips through on a single weak check.
Try it here
Check an image for editing or AI generation
Drop an image here
JPG, PNG, WEBP, GIF, BMP, TIFF, AVIF or HEIC
6 signals run together · 1 credit
1 credit per check
Run the same six signals the API runs — on an image of your own, with every family shown.
10 / 10Free checks left
See credit termsPreview of the output format. The signal scores and the verdict shown here are derived in the browser from the image’s name and size, not measured by the forensic engines — though the job flow and the corroboration rule are the real ones. Book a walkthrough to have images of your own run for real.
6 signal families are consulted on every image.
How it works
From a submitted image to an explainable verdict
- 01
Submit
Upload an image through the web app or any existing system
- 02
Review
The image is automatically examined for signs of editing or AI generation
- 03
Verify
Multiple authenticity checks help determine whether the image is AI-generated or not
- 04
Result and Evidence
Receive a clear real or fake result supported by easy-to-understand visual evidence
- 05
Report
View or download the result with a plain-language explanation of the findings
DeepImageGuard · workspace01 Submit
Interface simulation — an illustration of the workflow, not a live analysis.
- 01
Submit
Upload an image through the web app or any existing system
- 02
Review
The image is automatically examined for signs of editing or AI generation
- 03
Verify
Multiple authenticity checks help determine whether the image is AI-generated or not
- 04
Result and Evidence
Receive a clear real or fake result supported by easy-to-understand visual evidence
- 05
Report
View or download the result with a plain-language explanation of the findings
Product exclusiveness
Ordered by impact, most decisive first
Multi Layered Image Verification
Interface simulation — an illustration of the workflow, not a live analysis.
Market comparison
How DeepImageGuard compares with a single detection tool and manual review by an expert
Finds AI generated and edited images
- A single detection tool
- Partly — One method only
- Manual expert review
- Partly — Depends on the reviewer
- DeepImageGuard
- Yes — Several checks at once
Uses more than one kind of check
- A single detection tool
- No — One method, one score
- Manual expert review
- Partly — Based on judgement
- DeepImageGuard
- Yes — Independent checks combined
Looks for a trusted record of the original
- A single detection tool
- Partly — Sometimes
- Manual expert review
- No — Not available
- DeepImageGuard
- Yes — Included
Shows where the image looks altered
- A single detection tool
- No — Score only
- Manual expert review
- Partly — Written notes
- DeepImageGuard
- Yes — Visual evidence
Explains every result
- A single detection tool
- No — A score and nothing more
- Manual expert review
- Partly — Varies by reviewer
- DeepImageGuard
- Yes — Evidence and plain language
Protects Real images from false alarms
- A single detection tool
- Partly — Varies
- Manual expert review
- Partly — Human error possible
- DeepImageGuard
- Yes — Checks must agree
Gets better over time
- A single detection tool
- Partly — Occasional updates
- Manual expert review
- No — Not available
- DeepImageGuard
- Yes — Reviewed and refined
Handles large volumes without extra effort
- A single detection tool
- Partly — Varies
- Manual expert review
- No — Limited by people
- DeepImageGuard
- Yes — Runs inside your systems
The architecture
Several checks in, one clear answer out
No single check can decide on its own whether a photograph is genuine
Input
What goes in
One image · web app or your own systems
Accepted straight away
Received
While you carry on
The checks run in the background
Received, in progress, complete
Independent verification
- Visual characteristicsHow the picture itself looks
- Camera tracesWhat a genuine capture leaves behind
- Synthetic indicatorsSigns the image was generated
- File provenanceThe history the file carries
- Scene consistencyWhether the scene adds up
- Known-media matchingWhether it has been seen before
Corroboration
The checks have to agree
One check on its own is not enough. A result is only treated as highly confident when at least two independent checks point the same way, which is what keeps genuine photographs from being flagged by mistake.
Explainable output
Real or fake, with confidence
- Visual evidenceWhat was examined, shown on the image itself
- Plain language explanationWhich checks led to the result, and why
Use cases by sector
Use cases across sectors
Media & journalism
Verify photographs before publication and flag synthetic imagery.
Social & online platforms
Help trust-and-safety teams detect AI-generated images at scale.
How the sector deploys itE-commerce & marketplaces
Detect fake or AI-generated product and listing images.
Deployment strategy
Flexible deployment for secure image detection
Deploy DeepImageGuard in cloud environments for scalable image authenticity verification and easy access across distributed teams and applications.
Built around your infrastructure
Whether deployed in the cloud, within private infrastructure or across a hybrid environment, DeepImageGuard provides a consistent image authenticity verification capability while supporting enterprise security, scalability and integration requirements.
Four things an image check has to get right
How your teams reach it, what it accepts, how much you can run, and who is allowed to do what.
- How you use it
- A secure web application for teams who verify images by hand, and a connected option for organisations that would rather have image checks happen inside the systems they already work in.
- What it accepts
- Images in the formats organisations already work with, including JPG, PNG, WEBP, GIF, BMP, TIFF, AVIF and HEIC.
- How much you can run
- Each organisation is given its own secure access with a daily usage allowance, which can be set to match the volume you expect and raised as your needs grow.
- Who can do what
- Everyday users upload images, look back over their history, download results and share feedback. Administrators also manage people, access and how the service is kept up to date.
Frequently Asked Questions
DeepImageGuard examines every image in several independent ways at the same time instead of relying on one method. Each check looks for a different sign that an image has been edited or created by AI, and the findings are brought together into a single clear result of real or fake with a confidence level. Because the checks are independent, an image has to look convincing to all of them, not just to one.
Protecting real images is built into how the result is decided. A result is only treated as highly confident when at least two independent checks point the same way, so one uncertain reading is never enough on its own. Every result also arrives with visual evidence and an explanation in everyday language, so your team can see what was actually found rather than trusting a number.
The formats most organisations already work with are supported, including JPG, PNG, WEBP, GIF, BMP, TIFF, AVIF and HEIC. You can upload images through the secure web application or send them from systems you already use.
Yes. Alongside the web application, DeepImageGuard can be connected to the applications and workflows your teams already use, so image checks happen as part of normal work rather than as a separate task. Each organisation is given its own secure access with a daily usage allowance that can be set to match the volume you expect.
Yes. Difficult cases are reviewed by specialists and used to refine the service as new kinds of image manipulation appear, with careful checks before any change takes effect. Administrators oversee that process, while everyday users can flag any result they believe deserves a second look.
DeepImageGuard focuses on image authenticity alone: whether a photograph is genuine, edited or created by AI. DeepFraudGuard is the broader product that also covers document checks and identity document verification. If images are the only thing you need to verify, DeepImageGuard is the focused choice.
The rest of the line
More in SyntheticMediaGuard
Where it runs
Industries deploying DeepImageGuard
Each sector page covers the threat model, the controls, and the regulators that apply.
Find out whether a photograph is genuine
Have a solutions engineer walk your team through a verdict on an image of your own, and how the API drops into your pipelines.