-
AI in Action: Fighting Subtitle Piracy

Multimodal AI—OCR, ASR, NLP—plus watermarking and blockchain timestamps to detect, trace, and prove subtitle theft.
-
Multimodal Similarity for Content Protection

Detect edited UGC by combining visual, audio, and text similarity with invisible watermarking and blockchain timestamps.
-
How Multimodal AI Powers Content Protection

Multimodal AI, fingerprinting, watermarking, and blockchain timestamps detect cross-format reuse, prove ownership, and speed takedowns.
-
Scaling Multimodal AI for High-Volume Media Matching

Build shared embeddings, segment video, and use optimized vector search for fast, accurate media matching and enforceable ownership records.
-
5 Privacy Risks in Multimodal Content Matching

Five core privacy threats in multimodal content matching—identity linkage, biometrics, profiling, model leakage, and consent failures, with practical fixes.
-
5 Challenges in Multimodal Content Matching

Breaks down five core barriers—heterogeneous modalities, alignment, fusion, transformations, and scale—to reliable cross-format content matching.
-
Invisible Watermarking for Tamper Detection

Embed synchronized invisible audio‑video marks and blockchain timestamps to detect edits, sync drift, and partial tampering.
-
Invisible Watermarking for Video and Audio Content

Layered protection for video and audio: invisible watermarking, blockchain timestamps, forensic leak tracing, and AI matching.
-
Multimodal AI for Digital Asset Management

Multimodal AI transforms DAM with cross-format discovery, edit-resistant matching, integrated rights workflows, and immutable provenance.
-
Content Fingerprinting: Blockchain vs. AI Approaches

Blockchain for tamper-proof timestamps; AI for large-scale detection of edited copies—combine both for proof and monitoring.
