The AI Detection Approach
When deepfakes emerged as a serious threat, the natural response was to build better detectors. The logic seemed sound: if AI can create fake images, AI can detect them.
Major tech companies, governments, and research institutions invested heavily in detection technology:
- Pattern analysis: Looking for artifacts in AI-generated images
- Frequency analysis: Detecting unnatural patterns in image data
- Facial inconsistencies: Identifying subtle errors in deepfake faces
- Metadata forensics: Analyzing file metadata for manipulation signs
- Neural network fingerprinting: Identifying the "signature" of specific AI generators
Early results were promising. First-generation deepfakes had obvious tells: unnatural blinking patterns, inconsistent lighting, artifacts around hair and teeth. Detection models achieved 90%+ accuracy.
Then the generators improved.
The Arms Race Problem
AI detection faces a fundamental structural disadvantage: the same techniques that improve detection also improve generation.
Here's the cycle:
- Researchers publish detection methods
- Generator creators use these methods as training objectives
- New generators avoid the detected patterns
- Detection accuracy drops
- New detection methods are developed
- Repeat
This isn't speculation—it's documented history. The "blinking detection" method (deepfakes don't blink naturally) was published in 2018 and defeated within months. Every major detection advance follows the same pattern.
The Numbers Tell the Story
A 2024 meta-analysis of deepfake detection systems found:
- Detection accuracy on known generators: 85-95%
- Detection accuracy on new/unknown generators: 45-65%
- Detection accuracy after adversarial fine-tuning: 30-50%
In other words: detection works well against yesterday's fakes and poorly against tomorrow's.
The Fundamental Problem: Detection Is Probabilistic
Even perfect AI detection (which doesn't exist) would face an insurmountable problem: it can only provide probability, not certainty.
When a detection model says "95% confident this is fake," what does that mean practically?
- For legal evidence: Inadmissible. Courts require certainty, not probability.
- For news organizations: Insufficient. Publishing "probably authentic" photos risks reputation.
- For financial decisions: Dangerous. 5% error rate on millions of images means massive fraud exposure.
- For individual trust: Unsatisfying. "Probably real" doesn't restore confidence.
There's also the false positive problem. A 95% accurate detector applied to 1 million images will wrongly flag 50,000 authentic images as fake. This destroys trust in the detection system itself.
The Cryptographic Approach
Cryptographic verification takes a fundamentally different approach. Instead of analyzing images for signs of manipulation, it creates unforgeable proof at the moment of capture.
The logic is simple:
- At capture time, create a cryptographic hash of the image
- Sign this hash with a secure key
- Anchor the signed hash to an immutable record (blockchain)
- Any modification to the image changes the hash
- Verification is mathematical, not probabilistic
The Key Difference
AI detection asks: "Does this image look fake?"
Cryptographic verification asks: "Does this image match its original signed hash?"
The first question is subjective and can be fooled. The second is mathematical and cannot.
Comparing the Approaches
Accuracy
AI Detection: Variable, typically 60-95% depending on the fake quality and detector training. Degrades over time as generators improve.
Cryptographic Verification: 100% for images with valid proofs. Mathematical certainty, not statistical confidence. Does not degrade over time.
False Positives
AI Detection: Significant. Authentic images are regularly flagged as fake, especially images with unusual lighting, artistic editing, or from older cameras.
Cryptographic Verification: Zero. An image either has a valid proof or it doesn't. No false positives are mathematically possible.
Retroactive Application
AI Detection: Can analyze any existing image. This is its one major advantage—it works on historical content.
Cryptographic Verification: Only works on images captured with verification enabled. Cannot verify images captured before the system was implemented.
Adversarial Resistance
AI Detection: Vulnerable. Dedicated adversaries can craft images that fool specific detectors. Defeating detection is an optimization problem with known solutions.
Cryptographic Verification: Resistant. Breaking the system requires breaking the underlying cryptography (SHA-256, ECDSA, etc.)—problems that have resisted decades of attack by the world's best mathematicians.
Computational Cost
AI Detection: High. Running neural network inference on every image requires significant compute. Scales poorly.
Cryptographic Verification: Low. Hash verification is trivial—a smartphone can verify thousands of images per second.
Explainability
AI Detection: Black box. Models provide confidence scores but can't explain why they flagged an image.
Cryptographic Verification: Fully transparent. Verification either passes or fails based on clear mathematical criteria anyone can audit.
The "But What About..." Objections
"But Detection Works on Existing Images"
True, and this is AI detection's genuine strength. For analyzing historical content, detection is the only option.
But here's the thing: as we move forward, the vast majority of images that matter will be newly created. The question isn't "how do we verify photos from 2010?" but "how do we ensure trust in photos from 2025 and beyond?"
For that question, cryptographic verification is strictly superior.
"But Cryptographic Systems Can Be Compromised"
Any system can theoretically be compromised. The question is: how hard is it?
Breaking AI detection: Train a generator to fool the detector. Graduate-student-level work, commonly done in academic papers.
Breaking cryptographic verification: Find a collision in SHA-256 or break ECDSA. Would require a mathematical breakthrough that would also break all of internet security, banking, and cryptocurrency.
The security margins aren't comparable.
"But Not Everyone Will Use Verification Apps"
True today, but this is a adoption problem, not a technical limitation.
Consider the parallel to HTTPS. In 2010, most websites didn't use encryption. Today, browsers warn users about unencrypted sites, and HTTPS is essentially mandatory.
The same transition will happen with image verification. As verified images become the standard for trust-sensitive applications (journalism, legal evidence, insurance, etc.), unverified images will become inherently suspect.
"But AI Keeps Getting Better"
This is an argument against AI detection, not for it.
AI detection improves linearly while AI generation improves exponentially. The gap widens over time. Every advance in AI makes generation easier and detection harder.
Cryptographic verification doesn't face this asymmetry. It's based on mathematical hardness assumptions that have held for decades and show no signs of weakening.
The Hybrid Approach
The strongest practical approach combines both methods:
- Cryptographic verification as primary: For images with valid proofs, verification is definitive
- AI detection as fallback: For unverified images (historical content, images from non-verified sources), detection provides probabilistic guidance
- Clear user communication: "Verified authentic" vs. "Likely authentic (AI assessment)" vs. "Cannot verify"
This approach acknowledges AI detection's real utility while recognizing its fundamental limitations. Over time, as cryptographic verification becomes standard, the role of AI detection diminishes.
The Strategic Implication
Organizations investing in digital trust face a choice:
- Invest in detection: Fight an endless arms race, accept probabilistic results, rebuild systems as generators improve
- Invest in verification: Build infrastructure that provides mathematical certainty and doesn't require constant updates
Detection is a treadmill. Verification is a foundation.
For any organization where image authenticity matters—news organizations, legal systems, insurance companies, healthcare providers, financial institutions—the strategic choice is clear: build on cryptographic verification.
The Bottom Line
AI detection was a reasonable first response to the deepfake problem. But it's fundamentally the wrong approach—fighting AI with AI in a race the defenders always lose.
Cryptographic verification sidesteps the arms race entirely. Instead of asking "is this fake?" it asks "can you prove this is authentic?" That question has a definitive answer.
The future of digital trust isn't better detection—it's better proof. Organizations that understand this will build on foundations that last. Those that don't will rebuild forever.
About Rial Labs
Rial Labs provides cryptographic image verification using zero-knowledge proofs. Our system generates mathematical proof of image authenticity at capture time—no AI detection required, no probability estimates, just certainty.