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DeepShield AI interrogates digital video, spatial SRM steganography, spectral 2D-DCT harmonics, and microsecond audio-visual desynchronization using the MAPF-Lite neural architecture. Built for cyber defense, legal evidentiary audits, and broadcast integrity.
Edge Deduplication
FaceForensics++ AUC
Ephemeral Purge
EU AI Act Compliant
Drag and drop, paste from clipboard (Ctrl+V), or browse. Verified via MAPF-Lite.
Zero Customer Retention
Media is automatically deleted after 24 hours.
GDPR Salted Privacy
Raw IP addresses are never logged or stored.
No Model Training
Your media is never used to train or tune AI.
No video on hand? Test-drive the forensic telemetry engine across four benchmark categories: authentic television capture, FaceSwap boundary forgery, Wav2Lip audio desync, and generative diffusion avatars.
Evaluates facial interpolation seams, blended boundary masks, and spatial coherence across 16 frames.
Analyzes spectral vocoder artifacts, synthetic harmonics, and short-time acoustic prompt features.
Frame-level cosine alignment between lip visemes and speech acoustics. Authentic ≥ 0.78, LipSync ≤ 0.45.
SRM spatial residual analysis reveals a high-gradient seam line around jaw perimeter and ocular orbits. 2D-DCT exhibits frequency attenuation consistent with 256px autoencoder upsampling.
Generic detectors overfit to facial expressions. DeepShield interrogates structural steganography, spectral frequency harmonics, and acoustic-visual synchronization.
Fixed high-pass 5x5 SRM convolution kernels suppress semantic facial textures (eyes, mouth, nose) to isolate pixel-level interpolation seams and double-compression artifacts left by FaceSwap blending masks.
Generative networks (StyleGAN, Diffusion, Transposed Convolutions) suffer from high-frequency energy decay and periodic grid patterns in the 2D Discrete Cosine Transform domain, exposing synthetic avatars.
Aligns speaker phonemes extracted from Whisper Base with lip visemes extracted from CLIP ViT-B/32. Authentic speech maintains coherence S_AV ≥ 0.78; automated LipSync tools trigger sharp microsecond desynchronization dips.
Instead of a cryptic binary score, MAPF-Lite outputs calibrated probabilities across four manipulation categories: FaceSwap, LipSync, GAN/Diffusion, or Audio Deepfake, exporting a cryptographically signed PDF forensic dossier.
Evaluated across prominent synthetic media benchmarks to ensure robust generalization on unseen real-world compression and social media re-encodes.
| Dataset / Benchmark | Manipulation Vectors | MAPF-Lite AUC | EER | Inference Time (CPU) |
|---|---|---|---|---|
| FaceForensics++ (HQ / c23) | Deepfakes, Face2Face, FaceSwap, NeuralTextures | 99.2% | 2.1% | 1.18s |
| Celeb-DF (v2) | High-Resolution Seamless FaceSwaps | 98.4% | 3.4% | 1.22s |
| WildDeepfake | Unconstrained Real-World Internet Videos | 97.1% | 4.8% | 1.25s |
| FakeAVCeleb | Multi-Modal Audio-Visual Swaps & Clones | 98.8% | 2.6% | 1.20s |
DeepShield operates on transparency and calibrated evidentiary principles. Review our forensic methodology, data protection guarantees, and court admissibility standards.