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Shufti MCP
How Much of What You’ve Verified Is Actually Real?
Shufti’s Deepfake Blindspot Audit now runs fully inside your AWS environment, auditing historic KYC for manipulation signals without moving sensitive data off-prem.
Run Now on AWSDid You Know Deepfake Detection Is Now an Arms Race?
Deepfakes do not stand still. Threat actors test your defenses and come back smarter.
Shufti runs inside your AWS environment so you can fight back where it matters most. Detect manipulation in real-world conditions, move faster as threats evolve, and keep sensitive data inside your cloud.
Explore Clouds
Runs entirely
in your cloud
No PII
Ever leaves
No integration
or coding
Not All Synthetic Media is Dangerous Until It Becomes a Deepfake
Synthetic media covers all digitally altered or created content. However, deepfakes are a high-risk subset, fully AI-generated media designed to replicate real identities with near-photorealistic accuracy.
Why Detection Starts With The Right Distinction
Different manipulation types leave different signals. These signals are evaluated directly within your AWS compute layer, reducing noise introduced by external pipelines or data transfers.
The Comfort of Simple Answers
Commercial deepfake detectors are trained on clean, controlled data, not on deepfakes used in real-life threat scenarios like spoofing remote identity verification systems. Their performance claims often rely on threshold-based benchmarks that look impressive but create a false sense of coverage across real-world threat environments.
360° Deepfake Detection
with Shufti's Seven Gates of Defense
Gate 1
The Biometric Detective
The Biometric Detective
This gate examines whether a face follows natural human geometry as it moves. It measures spatial relationships that the human eye rarely tracks, looking for inconsistencies that tend to appear when synthetic faces try to mimic real anatomy over time.
Gate 2
The AI Signature Hunter
The AI Signature Hunter
This layer scans for statistical patterns commonly produced by AI-generated media. Instead of identifying a specific tool, it looks for broader characteristics that distinguish machine-generated content from camera-captured reality.
Gate 3
The Digital Archaeologist
The Digital Archaeologist
This layer analyzes how the media has been processed. Real images tend to degrade evenly, while manipulated ones often show uneven compression or editing traces that suggest parts of the image were altered separately.
Gate 4
The Frequency Analyst
The Frequency Analyst
This gate moves beyond visible pixels and analyzes the image in frequency space. It detects noise and spectral patterns that real sensors naturally create, but synthetic systems often replicate imperfectly.
Gate 5
The Texture Specialist
The Texture Specialist
This layer focuses on fine surface details such as skin texture and edges. It looks for repetition, smoothing, or loss of micro-variation that can occur when generative models simplify complex natural patterns.
Gate 6
The Degradation Expert
The Degradation Expert
Many attacks rely on poor image quality to hide flaws. This gate is designed to remain effective under compression, blur, low light, and noise, identifying signals that persist even when clarity is reduced.
Gate 7
The Pixel Inspector
The Pixel Inspector
This final gate examines how pixels interact with one another at high resolution. It looks for subtle discontinuities that can reveal how an image was assembled, especially where synthetic media struggles to maintain consistency at fine detail.
Preview
