Deepfake Detection
Defending Trust
in a Synthetic World
in a Synthetic World
We use an ensemble of neural networks capable of detecting fakes — including deepfakes generated by new models — even if they weren’t seen during training.
ACC=0.9848
PREC=0.9966
REC=0.9866
F1=0.9913
MCC=2.9614
Dataspike is
AI Models Detected
Comprehensive, consistent, and deeply integrated coverage across all major generation technologies, ensuring reliable insight and full operational clarity:
Inswapper and face swap applications
Replay attacks and overlay masks
Partial occlusions (glasses, hair) and digital manipulation
Social media filters and Instagram deepfakes
Midjourney generated content and character animations
Hedra video generation and face replacement
Style GAN variants
Dual approach
Dataspike's dual approach
Two Solutions for Two Attack Vectors.
We address both traditional and modern threats with purpose-built technologies:
We address both traditional and modern threats with purpose-built technologies:
SDK Integration
On-Device Processing
Solves the privacy and offline challenge
Embedded SDK runs locally on user devices
Zero cloud dependency or API calls
Complete data privacy - nothing leaves the device 3-20ms processing speed
Works offline and in airplane mode
API Integration
Real-Time Cloud Processing
Solves the streaming attack challenge
RESTful API calls to cloud processing engines
Real-time behavioral pattern analysis
WebRTC/streaming protocol integration
1-2ms processing speed per API call
Scalable cloud infrastructure
Dual approach
Why two approaches?
Different threats require different solutions:
Static content
On-device detection sufficient
Live streaming
Cloud-based behavioral analysis essential
Privacy requirements
On-device processing mandatory
Scale requirements
Cloud infrastructure necessary
Real-Time
Continuous real-time detection of deepfake and spoofed media across live audio, video, and document streams during active sessions
Unlike static photo or video uploads, this approach detects manipulation during live interactions, when attackers use deepfake masks, replayed videos, or synthetic faces.
Minimal ux impact
99.83% accuracy in face-
swap and replay detection
webrtc, rtsp, rtmp, mp4,webm
status in milliseconds,
1-2 ms/frame (Gpu/npu)
api, Saas, on-prem, sdk
no video storage —
metadata only
Deepfake Detection
on-device
On-device deepfake and spoof detection for audio, video, and documents, delivering low-latency risk signals without raw media leaving devices
This approach is especially valuable where speed, security, and autonomy are critical — such as in KYC, proctoring, or Web3 verification.
on-device
with no cloud
dependency
Almost Zero delay
3–20 ms
per frame
Scalable design
Android, iOS,
WebAssembly
Deepfake Detection
How it works
Pipeline
One frame lies - video tells truth we don’t judge on a single image.
We analyze motion and consistency across frames — processing each in milliseconds and intelligently skipping frames to stay fast, efficient, and fair.
We analyze motion and consistency across frames — processing each in milliseconds and intelligently skipping frames to stay fast, efficient, and fair.
Customer Flow Setup
How to deploy
SDK Integration
(On-Device)
Format
Native mobile SDKs (iOS/Android) + WebAssembly
Integration
Embed directly into your mobile app or web application
Effort
Standard SDK integration - no ML expertise required
Timeline
1-2 days for basic integration
Maintenance
SDK updates through standard app update process
User Device
Video Input
SDK Processing
Local Analysis
Result Returned
No Network
Required
API Integration
(Real-Time Cloud)
Format
RESTful API endpoints + WebSocket for streaming
Integration
HTTP/HTTPS API calls from your backend systems
Effort
Standard API integration - well-documented endpoints
Timeline
1-2 days for basic integration
Maintenance
Auto-scaling cloud infrastructure handles traffic spikes
Client App
Video Stream
API Call
Cloud Analysis
Behavioral
Processing
Real-Time
Response
other
Works seamlessly with other modules