Multi-model ensemble consensus and cryptographic provenance validation. Cross-validate images across neural detectors and C2PA credentials to expose synthetic media and subtle face manipulation.
What to look for
Single-model detectors can produce false results on fine-tuned diffusion models or compressed social uploads. Deep Forensic Boost evaluates images across multiple forensic vectors:
Generative diffusion models leave characteristic periodic ripple patterns in the spatial-frequency spectrum that evade visual inspection.
Ensemble cross-validation flags subtle inconsistencies between primary neural predictors and secondary consensus classifiers.
Decodes and validates digital signatures, hardware camera credentials, and generative tool assertions embedded in image metadata.
Detects micro-seams, blending boundaries, and asymmetrical corneal reflections introduced during face swapping or neural inpainting.
Targeted forensic checklists, generator signature scanners, and catfish verification guides.
Free, private, and deleted after your scan.
