AI Watermarking

AI watermarking embeds a hidden, machine-detectable signal into generated content — an image, audio clip, video, or block of text — so it can later be identified as AI-produced without changing how it looks or sounds to a person. Approaches differ by modality: pixel-domain watermarks like Google's SynthID survive cropping and compression better than visible tags, while text watermarking biases token selection into a detectable statistical pattern. It's distinct from visible logos and from C2PA content credentials, which attach signed metadata that can be stripped. For builders, watermarking is becoming a compliance and trust feature: regulations and platform policies increasingly expect AI content to be labeled, and provenance signals help you moderate, filter training data, and defend against misuse. Practical notes: watermarks vary in robustness — heavy editing, paraphrasing, or re-encoding can weaken or remove them — and detection often requires the provider's own verifier. Treat watermarking as one layer, not a guarantee, and check what your generation vendor actually applies.