Remember when getting a card graded meant waiting six months just to find out your Tolarian Academy was off-center? Yep, those were the good ol’ days of pre-Mirrodin MTG card authentication.
But despite the streamlining of card grading since the 2000s, the $5.4 billion trading card market still hit an eventual crisis. Specifically, when PSA received more cards in three days than they had in the previous three months, forcing them to temporarily suspend Value, Regular, and Express service levels in April 2021.
So far, we have augmented these demand struggles with scanning machines, newer protocols, and raw manpower. And today, a new player has emerged: artificial intelligence systems. With developments in current AI-based TCG authentication, we might just have the groundwork today for the next major card authentication paradigm shift.
From Eyeballs to Multi-Million Dollar Grading Industry
The authentication landscape currently operates on multiple levels, from traditional methods that most collectors encounter to cutting-edge systems deployed by major companies.
For the most part, traditional authentication remains the de facto method for grading TCG cards today. Most collectors and smaller shops still examine cards physically using magnifying glasses, measure centering with rulers, and rely on tactile assessment of card authenticity elements.
However, the logistics of manual grading become an impossible task once an entire dedicated business starts processing cards by the thousands. To support this, major authentication companies have invested heavily in digitization infrastructure. The Fujitsu Fi-8170 scanner, for example, has become common at companies like PSA and CGC, able to process 6,000 cards per hour through their custom automated feeding systems. These are not exactly specifically built for TCG authentication, but are instead industrial scanners with very high-grade optical components (300 DPI) for nigh-microscopic assessment of each individual card.
It was but an inevitability. When processing tens of thousands of cards monthly, maintaining consistent lighting, documentation, and review processes becomes impossible with purely physical methods. As such, the shift toward digital workflows reflects practical necessity rather than technological preference.
You can imagine the workflow of such an integrated system as follows:
· Physical examination: Direct card inspection under controlled lighting
· Digital scanning: High-resolution image capture for screen-based analysis
· Database creation: Building reference libraries for comparison
· Quality standardization: Reducing subjective variation between graders
So, more than just automation and sheer processing power, the most important thing about this industry development is actually the quality of data. After all, each scan is essentially a permanent record, an ultra-detailed scan of the article, which then enables quality control reviews. Keep this in mind, for this will be the backbone of the next step that artificial intelligence will soon take.
Adding More ‘Machine’ (Learning) to the Infrastructure
With all the computer vision algorithm developments over the last few decades, it is quite easy to conceptualize such a system directly integrated into the card scanning process. The main idea, as with all automation, is to be theoretically able to completely replace human expertise. As of the writing of this article, however, implementations would most likely remain limited to very rich organizations, or those that can take the temporary losses of trial and error.
This isn’t so different from how AI companions are built to simulate trust, attention, and responsiveness, as those are traits that matter in both emotional and technical interactions. Yes, their applications differ, but the underlying architectures share the same foundations. For example, an AI grading system uses high-resolution scans and pattern recognition to verify card authenticity, while an AI companion picks-up nuances in languages and emotional cues to build heartfelt conversations. Even in more niche use cases, like a nsfw AI chatbot built for private interactions, the same machine learning principles are used here by perception modeling, adaptive feedback, and user-specific memory. Whether it’s verifying a rare holographic card or maintaining a convincing emotional rapport, both systems depend on their ability to read signals and adapt with each example they come across.
Algorithmic ‘Card Package’ Architecture
While PSA is already using basic machine vision thanks to their Genamint acquisition in 2021, we are yet to see the full potential of this upcoming paradigm. For one thing, we’ve only had a glimpse of advanced authentication systems that could theoretically employ proven neural network architectures like ResNet and VGG. These systems train on millions of card images to know which cards are real, based on two major factors: visual characteristics and signature keypoints in the artwork. Then there are also other visual systems, like Oriented FAST and Rotated BRIEF, that can identify things regardless of angle, orientation, and rotation, making the process even more theoretically flexible.
Implementing these tools could go for a processing pipeline that is somewhat like this:
STEP 1: Raw scanned images undergo noise reduction filtering
STEP 2: Lighting normalization to create consistent analysis conditions.
STEP 3: Advanced edge detection algorithms map card boundaries with sub-pixel precision
STEP 4: Surface analysis systems that catalog every scratch and alteration
STEP 5: Create a comprehensive data profile based on all previous steps.
STEP 6: Repeat the entire process a million times in the blink of an eye.
Of course, given that these are not inherently designed specifically for card authentication, actual deployment would remain relatively unoptimized, at least for the first few years. That being said, streamlining should be easy (inevitable) once the financial incentive of this level of AI integration is fully realized.
The Natural Flow of Visual Identification Tech to TCG
Indeed, companies like Ximilar started to realize the value of their services for neural network-based card authentication around the same time as PSA’s AI integration. In 2022, it established its first Pokémon card search engine. Then, a year later in 2023, its official AI card grading system finally came to life in the form of the TCG identifier API.
As of 2025, the company currently maintains databases exceeding 20 million unique card records. If you need a reverse image search to match against authentic examples, you can just hit them right up. Even for very
recent releases like the infamous Cori-Steel Cutter from Tarkir: Dragonstorm (immediately banned from Standard only two months after release), these systems can analyze the Magic: The Gathering’s (MTG) signature identification elements like any pro-appraiser would. It can, for example, accurately detect the iconic (and microscopic) dot matrix rosette printing pattern of real MTG cards.
Other Integrations that Can Help
Aside from looking at paper thickness and ink density, ultraviolet and infrared spectroscopy devices are also convenient add-ons in card authentication via artificial intelligence. Yu-Gi-Oh! cards like the undying Ash Blossom & Joyous Spring (a very aggressive, full 3-copy staple since 2017), for example, authenticate mainly via the holographic foil stamp placement. By integrating these extra tools, you can verify paper texture against Konami's production standards. You can even analyze Secret Rare holographic patterns using their signature polarized light. Counterfeiters can’t easily replicate the angular light reflection characteristics of these high-rarity Yu-Gi-Oh! cards.
Competitive Implementation Concepts
While PSA and Ximilar have championed the analysis of print marks and surface characteristics, other competitors have taken a different approach in the realm of scanning tech + AI-based authentication.
CGC Cards, for example, uses forensic methods like Raman spectroscopy for non-destructive ink analysis, coupled with electron microscopy for paper fiber examination, and then X-ray fluorescence spectrometry to detect altered card surfaces. They also have multi-spectral imaging systems that capture cards under seven different wavelengths simultaneously. AI systems then augment all of these scanning tools via card attribution and preliminary condition assessment. With it, the company can at least scan 12,000-15,000 cards daily, which is still an impressive number despite PSA’s higher volume.
Card Dealer Pro, instead of just determining authentic cards, provides additional grading to the items via condition assessment and market value analysis. The system is still mostly similar to what PSA and Ximilar use, but it differs greatly in what they use it for.
What Else Can We Do with AI Augmented Scanning?
Current research into authentication technology suggests several emerging capabilities that could eventually transform card verification. First, the concept of "triplet learning" represents a significant advancement in how AI systems could distinguish authentic cards from very sophisticated counterfeits. Originally based on face recognition, it could be used to train on multiple card images: an anchor (original), several positives (same original), and several negatives (high-level counterfeits). This forces the AI to learn more nuanced distinctions that can potentially fool even the most seasoned of appraising experts.
On the more controversial side of things, Blockchain integration addresses a specific authentication vulnerability: manipulation of proof and resubmission fraud. Even if the scans themselves can show fakes, the databases themselves can be altered, and cards can be doctored between submissions. Immutable blockchain records would create permanent histories that reveal if a card has been previously graded, rejected, or altered, making it nearly impossible to game the system through multiple submissions or after-the-fact tampering. Its practicality is yet to be proven, of course, but it is still a serious concept worth considering.
And finally, the Holy Grail of on-site, instant card verification (without manual work) is already in the works via possible edge computing prototypes using NVIDIA Jetson Xavier devices. Early concepts already suggest reasonable-ish implementation and accuracy (at least from what can be read), perhaps allowing the eventual development of a hand-device for on-site verification.