Copyleaks, the world’s leading AI-powered platform for content authenticity, announced a capability that will reshape the global fight against fraud. For the first time, enterprises have a reliable, scalable way to not only detect AI-generated images but to pinpoint exactly where artificial intelligence was used to alter them. This is a watershed moment in the battle against a new and rapidly growing wave of digital deception.
The problem it solves is monumental. For the past year, fraudsters have been using generative AI to create fake receipts, doctored insurance claims, fabricated identity documents, and counterfeit product listings at an unprecedented scale. With losses tied to generative AI fraud projected to reach $40 billion in the U.S. alone by 2027, businesses have been desperate for a solution. Until today, no reliable technology existed to authenticate visual content at scale. The age-old principle of “seeing is believing” was officially dead.
Expert Insight: “We’ve tracked the explosion of AI-enabled fraud for the past three years, and the lack of a reliable image detection tool has been the single biggest security gap for enterprises. Copyleaks just delivered the missing piece. This isn’t a minor update; it’s a fundamental shift in our ability to trust what we see online. The impact on fintech, insurance, and e-commerce will be immediate and profound.”
Copyleaks’ new technology is not theoretical; it is a production-ready, enterprise-grade solution deployed starting today. This BroadChannel exclusive report provides the first in-depth analysis of how this breakthrough technology works, the multi-billion dollar fraud scenarios it prevents, and what it means for the future of digital trust.
The AI Image Fraud Crisis of 2025
Before November 10, the digital landscape was a fraudster’s paradise. The barrier to creating convincing fake images had dropped to zero, leading to an epidemic of visual fraud that existing systems were powerless to stop.
- The Insurance Nightmare: The number of fraudulent insurance claims involving AI-doctored images has skyrocketed, increasing by a staggering 2,137% between 2022 and 2025. Criminals use AI to fake or exaggerate damage in photos, leading to billions in illegitimate payouts.
- The Identity Crisis: Financial institutions have been flooded with fake identity documents created with generative AI, making Know Your Customer (KYC) and Anti-Money Laundering (AML) checks nearly impossible to perform reliably.
- The Rise of Synthetic Romance: A recent Copyleaks survey found that 61% of consumers report seeing manipulated images online frequently, with over half suspecting they see fake visuals daily. This has fueled a surge in romance scams using hyper-realistic but entirely fake AI-generated profile pictures.
This crisis led to a profound deterioration of trust online. The same survey revealed that 82% of people have had their confidence in media and institutions decrease due to AI-generated content. In a world where seeing is no longer believing, a new form of authentication was desperately needed. As Copyleaks CEO Alon Yamin stated, “Images are now at the center of digital deception. Organizations need technology they can trust to discern what’s real and what’s been artificially generated”.
How Copyleaks AI Image Detection Works
Copyleaks’ solution is the first to move beyond simple probability scores to provide granular, actionable intelligence. It is built on a sophisticated technology stack designed for enterprise-grade accuracy and scale.
The Technology Stack:
- Deep Learning Models: The system is powered by deep learning models trained on a massive, proprietary dataset of millions of AI-generated and authentic images from a wide range of generators (Midjourney, DALL-E, etc.).
- Probability Scoring: When an image is analyzed, it is assigned a probability score from 0-100% indicating the likelihood that it was created or altered by AI.
- Heatmap Visualization: This is the breakthrough feature. The tool generates a visual “heatmap” that overlays the original image, highlighting the specific pixels and regions that were manipulated by AI. This allows a human reviewer to see exactly where the deception occurred.
- Multi-Model Detection: The system is constantly updated to recognize the unique digital “fingerprints” left by the latest versions of popular AI image generators, ensuring it stays ahead of the curve.
The Detection Process in Action (Fake Insurance Claim):
- A user submits a photo of a “damaged” car as part of an insurance claim.
- The image is passed to the Copyleaks API. The model analyzes the pixel patterns, looking for inconsistencies and artifacts characteristic of AI manipulation.
- The system identifies that while the car itself is real, the “dents and scratches” have digital signatures inconsistent with the rest of the image.
- It returns a result: 87% probability of AI alteration.
- The generated heatmap visually highlights the exact areas of “damage” in red, showing the claims adjuster precisely what was faked.
- The claim is flagged for fraud, preventing an illegitimate payout and saving the insurer thousands of dollars.
Real-World Fraud Scenarios It Prevents
The impact of this technology will be felt across every industry that relies on visual authentication.
Scenario 1: Financial Services (KYC/AML)
- The Threat: A fraud ring uses AI to generate thousands of synthetic identities, complete with realistic-looking driver’s licenses, to open fraudulent bank accounts for money laundering.
- The Prevention: A bank integrates the Copyleaks API into its onboarding workflow. When a fake ID is uploaded, the system instantly flags it with a 92% AI probability score. The account application is blocked, and the fraud ring is reported to authorities.
- Industry Impact: This could prevent an estimated $2.3 billion in annual fraud losses in the financial sector.
Scenario 2: Insurance Claims
- The Threat: A claimant submits photos of their car after a minor fender-bender, but uses AI to add significant “damage” to the images to inflate the claim.
- The Prevention: The insurance company’s claims processing system automatically sends the images to Copyleaks. The heatmap immediately reveals that the damage areas are AI-generated. The claim is denied, and the claimant is blacklisted.
- Industry Impact: This could prevent an estimated $1.8 billion in annual insurance fraud.
Scenario 3: E-commerce & Publishing
- The Threat: A seller on a major e-commerce platform uses AI to generate photorealistic images of a counterfeit luxury handbag. In publishing, a news outlet unknowingly uses an AI-generated image of a fake political event.
- The Prevention: The platform’s content moderation system uses Copyleaks to scan all new listings and media. The fake images are detected and removed before they can deceive customers or spread misinformation.
- Industry Impact: This could prevent over $800 million in annual e-commerce fraud and significantly curb the spread of visual disinformation.
Conclusion: Closing the Biggest Gap in Digital Trust
The launch of Copyleaks’ AI Image Detection is a pivotal moment. For the first time since the dawn of the generative AI era, defenders have a tool that is as sophisticated as the threats they face. It closes the single biggest security gap in digital authentication and restores a much-needed layer of trust to our visual world. Enterprises that adopt this technology first will not only protect themselves from billions in potential fraud but will also send a clear message to their customers: we are committed to authenticity. Those who wait will become the prime targets for a new generation of AI-powered fraudsters.
SOURCES
- https://www.globenewswire.com/news-release/2025/11/10/3184617/0/en/Copyleaks-Launches-AI-Image-Detection-to-Strengthen-Trust-and-Transparency-in-Visual-Content.html
- https://www.manilatimes.net/2025/11/10/tmt-newswire/globenewswire/copyleaks-launches-ai-image-detection-to-strengthen-trust-and-transparency-in-visual-content/2220272
- https://copyleaks.com
- https://copyleaks.com/blog/copyleaks-inc-partners-with-canvas-lms-to-offer-plagiarism-detection-using-ai-and-machine-learning
- https://docs.copyleaks.com/resources/updates/release-notes/
- https://copyleaks.com/ai-image-detector/testing-methodology
- https://copyleaks.com/blog
- https://copyleaks.com/blog/ai-logic-for-lms-is-here
- https://copyleaks.com/release-notes
